Keywords: Actuation and Actuators, Modeling, Identification, Calibration, Manipulation Planning and Control
Abstract: This paper aims to develop the low-carbon precise pneumatic servo plug tray seedlings machine. In the mechanism design, a rod-less pneumatic cylinder is selected as the actuator, equipped with a plug conveying platform combined with a needle seeding mechanism, and a soil perforating mechanism. SOLIDWORKS was adopted to design the low-carbon precise pneumatic servo plug tray seedlings machine. Mathematical models of the pneumatic servo system are established and analyzed. The functional approximation method with sliding mode control was formulated to control the designed machine. A PC-based system with MATLAB/SIMULINK was developed to achieve real-time precise seeding process. High precision seeding was realized at a speed of 120 trays per hour, with a single-seeding rate up to 99% and a missed-seeding rate of less than 3%. Also, the feasibility of the system was verified through germination rate experiments. Finally, carbon emissions of this designed machine were calculated. Maximum decreasing carbon emission of 80% compared to motors was achieved by the pneumatic servo plug tray seedlings machine. Therefore, results demonstrated superior performance of the proposed design in terms of seeding accuracy, operational efficiency, and energy consumption, establishing its potential as a viable solution for sustainable and scalable automated seeding applications.

Keywords: Legged Robots, Dynamics and Control
Abstract: Low-cost legged robots typically handle all computation tasks with a single microcontroller, which could get overloaded without proper optimization. To tackle this challenge, this paper presents a lightweight control architecture and its implementation on Q8bot, a miniature quadruped platform developed by the authors. The proposed method is optimized for rapid prototyping and experimentation on legged robot gaits, encompassing off-board trajectory computation, responsive wireless control, and quasi-real-time data acquisition from the untethered robot. The software implementation resulted in easily-tunable, dynamic locomotion of the robot as well as convenient sensor data visualization. The robot achieved a stable trotting speed of 0.3 meters per second, and a turning speed of 5 radians per second.

Keywords: Legged Robots, Modeling, Identification, Calibration, Multisensor Data Fusion
Abstract: Conventional state estimation methods for legged robots assume stable foot contact and rely on force sensors. However, in dynamic locomotion, these assumptions often break down due to foot slippage and body impacts, leading to significant estimation errors. This paper proposes a probabilistic state estimation framework that operates without contact sensors, integrating foot contact inference, slip detection, and body impact estimation into a unified model. Contact state estimation is performed using a momentum-based disturbance force estimation method, while slip state estimation distinguishes between sliding and stick-slip. Additionally, body impact states are probabilistically estimated based on angular velocity, linear acceleration, and body tilt information. The proposed framework is validated through simulations under various ground friction conditions and step times. Compared to conventional contact-based estimation methods, the proposed method reduces position estimation errors by 81.8% in the plastic foot friction model and 75.9% in the rubber foot friction model. Furthermore, the velocity errors are reduced by 51.3% and 47.1% in plastic and rubber surface conditions, respectively.

Keywords: Modeling, Identification, Calibration, Force and Tactile Sensing, Manipulation Planning and Control
Abstract: This paper presents the simulation and validation of a generalized spacecraft and robotic platform for In-Space Servicing, Assembly, and Manufacturing (ISAM) missions. A multibody dynamics model used for simulating ISAM missions is created with a combination of Matlab scripts and Simulink modeling with the intent of modular testing of different satellite buses and manipulators. The resulting model is validated using an experimental setup using a Stewart platform and a Universal Robots manipulator, demonstrating the similar results in the base reaction forces and torques with a matched trajectory.

Keywords: Motion Planning and Obstacle Avoidance, AI Reasoning Methods for Robotics, Behavior-Based Systems
Abstract: Autonomy implies the ability of an agent to be self-directing, thus independently handling tasks and responding to stimuli, as well as make their own decisions. Current mobile social robots are not autonomous as demonstrated by the frequently observed Frozen Robot Problem (FRP) as demonstrated when a robot freezes when encountering crowds and unexpected pedestrian behavior. Developing an autonomous system that can naturally react to such unplanned experiences is both an interdisciplinary and integration activity that involves sensing, artificial intelligence (AI), and robotics. In this paper, the authors document the development of a modular testbed whole system approach to autonomous mobile robots, their modeling and simulation (M&S), and demonstrate the system. The system developed herein leverages a biologically inspired cognitive architecture for decision making and memory through the Semantic Pointer Architecture (SPA) for context-based decision making and motor control for a robot. Through this approach, the autonomous system thus employs reactive goal-driven navigation which enables a robot to move toward a desired location by dynamically avoiding obstacles without relying on traditional path planning. Results are presented from M&S through factorial experimental designs to explore architecture and environment complexity as well as live testing in similar environments. The entire approach presented herein provides a framework for developing, testing, and validating autonomous robotics systems.

Keywords: Robotic Systems Architectures and Programming, Robotics in Hazardous Applications, Manipulation Planning and Control
Abstract: Future missions to explore the lunar and Martian surfaces will have an increased need for intravehicular robotics to assist with routine tasks, such as inspection, maintenance and repair activities, and logistics handling. Offloading some of these tasks to robots will leave more time for astronauts to perform tasks that specifically require astronaut expertise, such as science and exploration. To facilitate the development of intravehicular robotics technology for researchers, NASA has created the Integrated Mobile Evaluation Testbed for Robotics Operations (iMETRO) facility. This facility consists of high-fidelity intravehicular mockups, an example robot, configurable operator paradigms, and open-source software infrastructure. Using these resources, the iMETRO team developed and carried out an example demonstration to show how partners can build on top of the facility infrastructure for their custom applications. Future work with this facility will provide a dynamic simulation environment and introduce new robotic platforms to further advance the resources available to develop new technologies.

Keywords: Robotics in Hazardous Applications, Legged Robots, Robotic Systems Architectures and Programming
Abstract: The present research presents a control system for a quadruped robot based on software architecture made in ROS2. This system is designed to allow precise and stable navigation of the quadruped in complex and potentially hazardous industrial environments, under the supervision of an operator. The project integrates inverse and direct kinematics algorithms for position and orientation control, allowing the robot to adapt to different configurations and needs. In addition, an intuitive graphical user interface (GUI) was designed and implemented, which allows remote control of the robot's functions, such as position, orientation and gait speed, while monitoring its environment and configuration in real time, which together with its adaptability results in reliable industrial monitoring. Validations and tests are presented both in virtual environments and with the real robot, which demonstrated its adaptability and accuracy. This work contributes to the advancement of mobile robotics applied to industry, highlighting the use of quadrupeds as effective tools for monitoring, inspection and operations in areas of difficult access or high risk.

Keywords: Mechanism and Design
Abstract: This study presents the design, development, and evaluation of a low-cost mobile manipulator system that integrates a differential-drive mobile base with a 4-DOF robotic manipulator. Constructed entirely from off-the-shelf components, the system combines affordability with robust functionality, making it suitable for research, education, and low-cost industrial applications. The manipulator employs a 4-DOF RRRR configuration, powered by 12V DC motors and equipped with a gripper capable of 180-degree rotation. The mobile base features a differential-drive system that ensures precise navigation along predefined paths, supported by trajectory planning and kinematic modeling. Experimental investigations validated the system’s ability to execute coordinated navigation and manipulation tasks. The manipulator achieved a full range of motion in under 33 seconds, closely following desired joint angles with minimal error. The mobile base demonstrated accurate path-following capabilities, reconstructing global trajectories from encoder feedback. Despite the success, structural limitations due to the manipulator’s weight and reliance on motor encoders for feedback highlight areas for improvement. This work contributes to the advancement of cost-effective robotic systems capable of addressing real-world challenges in dynamic environments.

Keywords: Mechanism and Design
Abstract: This paper presents a novel autonomous robotic harvesting cell designed for hydroponic plant cultivation within a compact, self-contained system. The system integrates a robotic manipulator mounted on a gantry mechanism, enabling precise and automated pruning and harvesting operations. An RGB-D camera supports real-time object detection, while a custom-designed cutter--integrated directly into the manipulator--eliminates the need for additional actuators. The hardware prototype serves as a small-scale autonomous farming unit, demonstrating the feasibility of fully automated crop management in controlled environments. As an initial application, the system incorporates vision-based detection of tomato fruits and suckers. To facilitate continued development, a simulation model was created in Unity 3D, supporting virtual prototyping, system implementation, and algorithm evaluation prior to physical deployment. By combining advanced automation with a compact, modular design, the proposed robotic harvesting cell offers a scalable solution for indoor farming, urban agriculture, and small-scale food production.

Keywords: Mechanism and Design, Humanoids
Abstract: This paper introduces a multi-axis power transfer mechanism that is applicable when actuators and their corresponding joints are placed separately in a manipulator. In particular, in this work, the humanoid arm is considered the primary target of application. A modified Hobson joint, termed the Multi Angular Gearless Transmission (MAGT), serves as the central mechanism. The system transmits power through the elbow to the wrist without requiring direct placement of the actuator in the distal part of the manipulator. The design uses a layered MAGT (L-MAGT) to convey coaxial inputs. In this study, L-MAGT which uses three layers is applied to implement a wrist with three axes. After L-MAGT transmits inputs, the coupled wrist 3 axes structure converts them into roll, yaw, and pitch motion of the wrist. This approach reduces the weight of the actuator in the distal part of the manipulator. The weight reduction in the distal part leads to more agile manipulation of the manipulator. Furthermore, the MAGT ensures constant velocity transmission across varying elbow angles, eliminating the velocity fluctuation typically associated with universal joints, such as the Cardan joint. Experimental validation using a 3D printed prototype and a motion capture system shows that the mechanism achieves its intended range of motion and maintains acceptable repeatability, despite some backlash and compliance introduced by 3D printed components. The results confirm the feasibility of this mechanism for humanoid arms and manipulators. The proposed mechanism, which passes through a joint with variable angles, offers a novel multi-axis power transfer mechanism solution to manipulator design.

Keywords: Modeling, Identification, Calibration, Mechanism and Design, Soft Robotics
Abstract: Spherical mobile robots are rarely constructed with soft outer shells; even fewer contain hardware to control their inner pressure with compressors and solenoids. This paper presents a specific subsystem of a one-of-a-kind inflatable spherical robot that allows it to control its internal pressure. The design and packaging of the pneumatic outer shell and internal air management system are discussed. A novel dynamic model based on exponential change laws will be introduced to continuously track the change in pressure over time due to leaking. The model's goodness will be investigated when compared against empirical data from the robot and used in tandem with a pressure control scheme. Additional validation tests will empirically characterize the effects of potentially using pressure to influence control over an induced bouncing mode.

Keywords: Modular Robots, Robotic Systems Architectures and Programming, Behavior-Based Systems
Abstract: We present a novel approach for plug-and-play robotics that allows integrating modular payloads into a skills-based task planner and execution engine of an autonomous robot. This integration occurs seamlessly without rebooting the host, restarting processes, or requiring user input. The core innovation is packaging payload operational and behavioral software — e.g. hardware drivers, support libraries, executable skills and domain-specific ontologies — within the payload itself. Upon physical connection to the host’s data port, the payload components are automatically detected, transferred, and incorporated into the host’s software stack in real-time. This approach eliminates downtime, manual integration steps, and the need for expert knowledge, significantly enhancing robot adaptability and operational flexibility. Furthermore, the approach leverages popular open-source software to ease and encourage its adoption by developers. The method was demonstrated on a mobile robot that integrated a modularized commercial device, automatically ingested new skills from it, and used those skills to complete a simple task that was beyond its previous capabilities.

Keywords: Wheeled Mobile Robots, Mechanism and Design
Abstract: This paper presents the development and evaluation of a mobile robotic platform for autonomous crop field scouting and soil sensing. The system combines a durable commercial chassis kit with custom 3D-printed casings, enabling reliable operation across diverse outdoor field environments. The robot features encoder-controlled motors and a swivel-mounted front frame, allowing versatile and agile navigation through narrow crop rows and uneven terrain, as demonstrated in field trials conducted in cotton and peanut fields. A soil sensing mechanism, driven by a 360degree servo motor and employing a linear gear-and-rack mechanism, enables consistent soil penetration. Integrated with a low-cost 7-in-1 soil sensor, the platform provides real-time mapping of key soil parameters—nitrogen, phosphorus, potassium, electrical conductivity, pH, temperature, and moisture—to support data-driven farm management decisions. Preliminary experiments evaluated the robot's field navigation and soil sensing performance. Results demonstrate the potential of the platform for low-cost, mobile soil sensing, while also highlighting limitations in the current sensor's accuracy.

Keywords: Wheeled Mobile Robots, Motion Planning and Obstacle Avoidance, Object Recognition
Abstract: Manual roadside weeding poses significant safety risks. Deploying a robot for this task can mitigate these risks while reducing labor costs. While most previous research on weeding robots has focused on agriculture and gardening, little attention has been given to roadside weeding. In this study, we developed an autonomous weeding robot that navigates along the shoulder of paved roads and removes detected weeds. The robot utilizes an RGB camera to recognize road boundaries and follows the road edge autonomously. Additionally, an RGBD camera is used to measure weed positions and facilitate precise cutting.

Keywords: AI Reasoning Methods for Robotics, Robotic Systems Architectures and Programming, Manipulation Planning and Control
Abstract: Advancements in task planning and human-robot collaboration are driving innovation in robotics. The emergence of sophisticated artificial intelligence (AI), particularly large language models (LLMs), presents significant opportunities for enhanced robotic autonomy and flexible collaboration. In this work, we propose a system that leverages an LLM to interpret natural language prompts and generate task plans. This process integrates environmental data from a vision-language model (VLM) and utilizes an action-function library defining the robot’s capabilities. In addition, we develop an intuitive graphical user interface (GUI) that not only connects to the AI task planner, but allows for user oversight throughout the planning and execution process. To validate our approach, we conducted experiments using a dual-arm robotic system to perform a complex, multi-step task: installing a wire onto a power supply. Our system demonstrates significant potential for improving task flexibility and adaptability in human-robot collaborative settings. These findings pave the way for more autonomous and versatile robotic systems in industrial and collaborative applications.

Keywords: Robotic Hands, Rehabilitation and Healthcare Robotics, Mechanism and Design
Abstract: This paper presents a novel approach to the design and fabrication of powered prosthetic hands using Fused Deposition Modeling (FDM) enhanced by 4D printing principles. To overcome limitations in conventional methods, such as high part count, weight, and assembly complexity, and inherent drawbacks of FDM, such as large joint clearances and anisotropic strength, the study introduces thermally responsive self-adaptive mechanisms after printing. Two key mechanisms are proposed: a self-tightening RCJ that minimizes joint clearance and a self-aligning bending/twisting unit that modifies print orientation via controlled post-print deformation. These mechanisms are integrated into a fully 3D-printed prosthetic hand, eliminating the need for assembly. Experimental validation demonstrates improved mechanical precision and structural adaptability, highlighting the potential of this strategy to enable low-cost, customizable, and functionally robust prosthetic devices through single-step

Keywords: Performance Evaluation and Optimization, Rehabilitation and Healthcare Robotics
Abstract: Care robots are defined as robots or devices that use robot technology to provide physical and mental assistance to the elderly or disabled who have difficulty maintaining their daily lives. Currently, care robots are being developed for the purpose of providing various daily life assistance to care recipients (disabled people including industrial accident disabled people, elderly people, etc.) and caregivers (caregivers, family members, etc.). From 2019 to 2021, our research institute conducted safety-related research projects on four types of care robots (transfer, defecation, bedsores and posture change, meals) as part of a Ministry of Health and Welfare project, and the projects were successfully completed. However, as time passes and technological development advances, the products need to be improved. In addition, five types of care robots (indoor movement, bathing assistance, flexible wearable, communication, and integrated monitoring) have been added for projects to be conducted from 2023, and research and development has begun. Therefore, it is time to improve existing products and evaluate additionally developed products. In this study, we derive performance test items and apply test methods for performance evaluation of care robots, so that it can be used as a standard to confirm the performance and safety of the functions of care robots. Performance test items are intended to provide test results by setting evaluation criteria, including the functions of each robot and the intended use scenario.Companies developing care robots will be able to derive product improvement points through company feedback through the results of this study. Since the test items were applied through pilot tests at the current prototype stage, not all performances of actual commercialized products or care robots with new functions can be covered in this study. However, we believe that it can be used as a good reference for developing performance test methods for the relevant performance.

Keywords: Industrial Robots, Robotic Systems Architectures and Programming, Contact: Modeling, Sensing and Control
Abstract: To autonomously collect soil in uncultivated terrain, robotic arms must distinguish between different granular materials and penetrate the correct material. We develop a prototype that collects soil in heterogeneous terrain. If mounted to a mobile robot, it can be used to perform soil collection and analysis without human intervention. Unique among soil sampling robots, we use a general-purpose robotic arm rather than a soil core sampler.

Keywords: Robot Surveillance and Security, Robotics in Hazardous Applications, Legged Robots
Abstract: Infrastructure inspection is becoming increasingly relevant in the field of robotics due to its significant impact on ensuring workers’ safety. The harbor environment presents various challenges in designing a robotic solution for inspection, given the complexity of daily operations. This work introduces an initial phase to identify critical areas within the port environment. Following this, a preliminary solution using a quadruped robot for inspecting these critical areas is analyzed.

Keywords: Multi-Robot Systems, Wheeled Mobile Robots
Abstract: Task allocation and path planning are critical challenges in coordinating heterogeneous multi-robot systems for agricultural applications. This research focuses on automating lavender harvesting, where robots with varying capabilities must collaboratively navigate complex field layouts to efficiently complete harvesting tasks.

We propose two heuristic approaches to address the specific problem of multi-robot coordination for lavender harvesting. The first approach utilizes a Large Language Model (LLM) to generate harvesting plans. By providing the LLM with small-scale examples and iteratively refining prompts with detailed descriptions of task attributes, robot capabilities, and environmental conditions, it produces feasible task allocations and paths for each robot while minimizing overall operational time. The second approach employs a greedy heuristic algorithm, which starts with an initial feasible solution and iteratively improves it by optimizing task allocation and robot paths while ensuring all constraints are satisfied. This method guarantees feasibility by directly incorporating task requirements and robot constraints into its optimization process.

Both approaches are validated through simulations of lavender fields with varying sizes, layouts, and numbers of robots. The results demonstrate the effectiveness of these methods in achieving efficient task allocation and path planning for heterogeneous multi-robot systems. This work highlights the potential for these approaches to advance automation in lavender harvesting, contributing to increased efficiency and sustainability in agricultural operations, further considering fuel and payload constraints as well as required harvesting techniques.

Keywords: Humanoids, Mechanism and Design, Wheeled Mobile Robots
Abstract: Balloid, a compact ball-shaped humanoid robot for teaching robotics to middle/high school students, is introduced in this study. With its hybrid design, it can switch between walking on its two legs and driving using its separately-driven wheels, allowing it to navigate a variety of surfaces better than today’s educational robots. This paper covers Balloid's mechanics and control system. The mechanical design and building section describes the construction of Balloid's legs and shoulder-mounted wheels. The control system development section explains the developed software that uses trigonometry to calculate Balloid’s lower body actions and uses the differential drive control to make its upper body movements. In addition, experimental results for standing and driving are presented. Future plans include exploring a momentum-based rolling mode for better energy efficiency. This project aims to provide students with hands-on experience that bridges the gap between simple wheeled robots and humanoids. Balloid’s mechanical design and construction details will be shared openly for people to use and modify free for STEM education.

Keywords: Contact: Modeling, Sensing and Control, Wheeled Mobile Robots
Abstract: In this study, we propose a floor material classification method under driving motion using a piezoelectric actuator– sensor pair. The piezoelectric pair consists of an actuator and a sensor. When the pair contacted to the floor while driving motion, the actuator was operated and the sensor collected signals from the floor simultaneously. The collected signals were preprocessed to change it as input data of machine learning. With this method, we classified six floor materials and one no contact situation as a high accuracy of 95.4%.

Keywords: Autonoums Vehicle Navigation, Physical and Cognitive Human-Robot Interaction, Dynamics and Control
Abstract: This study introduces Memory-Augmented Model Predictive Controller (MAMPC), which enhances safe navigation in cluttered environments during human-following scenario by utilizing a buffer of previous optimal control inputs and their associated costs. By strategically reusing partial solutions and refining critical segments of the prediction horizon in real time, MAMPC demonstrates superior obstacle anticipation and collision avoidance compared to traditional Model Predictive Controllers across various complex scenes.

Keywords: Actuation and Actuators, Legged Robots, Mechanism and Design
Abstract: This study proposes a Surface-mounted Permanent Magnet Synchronous Machine (SPMSM) design strategy for applying Field Weakening Control (FWC), a technique commonly used in electric vehicles, to actuators in quadruped robots. By extending the motor's speed range, FWC enables higher locomotion speeds without exceeding voltage limits of battery. We analyze the key motor characteristics required for effective FWC implementation in legged robot actuators and validate our approach through RAISIM simulations with reinforcement learning (RL). The results demonstrate that FWC can significantly enhance maximum locomotion speed of HOUND2 from 11.0 m/s to 14.0 m/s in simulation, providing a foundation for high-performance legged robot actuation.

Keywords: Haptics
Abstract: Although research into the use of shape displays as 3D design tools exists, the most appropriate and versatile haptic technologies for such a task are costly and require a high level of peripheral electronics that act as a barrier to scalability, making their integration into appropriate settings difficult. Additionally, the resultant niche nature of this technology means that the different tools and methods for conveying 3D information using haptic feedback are largely unrealized and under-reviewed. This work presents the results of the first phase of a broader set of works concerning surface haptic displays. We evaluated a 'tactile height map' method of conveying 3D information between two different pin array configurations, consisting of low- and high-density arrays.

A user study exploring how users perform when assembling 3D objects using this 'tactile height map' method and these pin arrays found that pin density resulted in a significant difference in the time it took to reassemble a 3D model; however, it did not significantly affect assembly accuracy. These results can inform how future commercial surface displays can be leveraged to support complex design tasks, such as 3D modeling, effectively. Our future research adds a rendering method and expands the number of haptic surface displays to determine how 3D information can best be conveyed using tactile feedback as the main feedback mechanism.

Keywords: Robot Surveillance and Security, Robotics in Hazardous Applications, Search and Rescue Robotics
Abstract: An end-to-end learning approach for snake robot locomotion using deep reinforcement learning is proposed in this paper. The reward functions tailored to each gait of a snake robot were designed by leveraging the natural locomotion patterns of snakes. The comparative analysis between a reinforcement learn-ing-based control and conventional cyclic control was discussed in this research.

Keywords: Mechanism and Design, Wheeled Mobile Robots
Abstract: This work-in-progress extended abstract introduces CLAW II, a novel wheel-less legged mechanism that maintains smooth-rolling motion and obstacle-climbing capabilities without a conventional wheel. Building on CLAW I, which integrated a circular wheel with leg segments, CLAW II eliminates the wheel entirely, relying solely on the curved leg geometry for continuous rolling-like motion while reducing weight and mechanical complexity. To validate CLAW II, we are developing a mobile robot equipped with CLAW II mechanisms. This robot will be tested for obstacle climbing, multi-terrain mobility, and seamless rolling motions.

Keywords: Biomimetic and Bioinspired Robots, Dynamics and Control, Legged Robots
Abstract: Wall-climbing robots face significant challenges in maintaining stability during climbing. This study presents a bio-inspired tail mechanism that enables a quadrupedal robot to self-right instead of falling when encountering instability. Inspired by the biomechanics of lizards, the proposed mechanism leverages dynamic tail actuation to reorient the robot’s body against vertical surfaces to prevent it from falling. In this work, the mechanism for this tail and the inverse kinematics calculations for position control are discussed.

Keywords: Dynamics and Control, Modular Robots
Abstract: This paper introduces the dynamic rolling locomotion of the Variable Topology Truss (VTT) system. While existing research has explored motion planning and control of truss systems, including our previous work on rolling locomotion and path planning, these studies primarily focused on quasi-static motion - a methodology that inherently limits locomotion speed and efficiency. We are developing a rolling locomotion method that can maintain the VTT system's momentum during locomotion. A preliminary version of the rolling algorithm was tested in a simulation environment and the results were analyzed. Our findings establish the foundation for enhancing the locomotion capabilities of the VTT system and provide critical insights for future hardware implementation.

Keywords: Multi-Robot Systems, Range, Sonar, GPS and Inertial Sensing, Aerial and Flying Robots
Abstract: External factors, including urban canyons and adversarial interference, can lead to Global Positioning System (GPS) inaccuracies that vary as a function of the position in the environment. This study addresses the challenge of estimating a static, spatially-varying error function using a team of robots. The central idea is to use sensed estimates of the range and bearing to the other robots in the team to estimate changes in bias across the environment. This abstract describes a work-in-progress algorithm for this problem that uses a quadratic optimization formulation to find a self-consistent set of pointwise bias estimates, followed by a Gaussian Process Regression (GPR) to form a bias map estimate across the full environment. We also describe an approach that uses informative path planning techniques to plan movements for the robots to improve the accuracy of these estimates. Preliminary results in simulation show the promise of the approach.

Keywords: Search and Rescue Robotics, Aerial and Flying Robots, Deep Learning for Visual Percepton
Abstract: This extended abstract presents a deep learn- ing–guided fire extinguishing system aimed at mitigating wild- fires under adverse conditions such as strong winds, nighttime, drought, and smoke. The system combines a YOLO-based object detection algorithm with a unique Nona Filter to enable real-time recognition and priority-based target tracking of fires and smoke. In controlled experiments, the GFED system successfully identified and tracked fire sources at distances up to 30 meters, maintaining consistent performance even when multiple fire and smoke instances appeared in the same frame. The current work will target aerial deployment under challenging environments like strong winds, nighttime, high altitude operations. Additional enhancements include compu- tational fluid dynamics, 6-degree-of-freedom analysis, sensor integration, and further optimization of the Nona Filter. With continued development, GFED shows strong potential to evolve into a fully autonomous wildfire suppression solution.

Keywords: Human-Robot Augmentation
Abstract: Introductory robotics courses often rely on theoretical lectures, occasionally supplemented by physical labs that offer hands-on experience. However, such labs are costly and resource-intensive, making them impractical for many classroom settings. To overcome these limitations, we introduce RAISE (Robotics with Augmented Instruction for Student Engagement), an Augmented Reality (AR) application designed for standard mobile devices. RAISE overlays 3D robot models onto real-world environments, enabling students to interactively explore core robotics concepts--such as rigid-body motion and forward kinematics--while visualizing coordinate frames and screw axes in real time. By leveraging AR, the platform aims to enhance conceptual understanding and engagement beyond traditional methods without cost and resources required for physical labs. Future work will evaluate its educational impact by comparing RAISE-enhanced instruction with conventional lecture-based approaches using a range of learning metrics. Planned technical updates include support for additional robot models, user-designed assemblies, advanced analytics, and expanded coverage of more complex robotics topics—positioning RAISE as a comprehensive, accessible, and adaptable educational tool.

Keywords: Underwater Robotics
Abstract: Relying on RGB cameras for underwater robotics presents challenges, particularly due to varying light conditions that reduce image processing effectiveness. This work-in-progress paper explores image focus detection methods designed for dynamic underwater environments. We use the Laplacian operator to measure focus and evaluate its effectiveness through lab and underwater experiments with the Reach Alpha 5 manipulator arm. Our goal is to enable underwater robots to dynamically adjust camera positioning for clearer imaging. While effective in many scenarios, the method requires manual intervention due to the lack of standardized thresholds and reliance on raw Laplacian values. Future improvements, such as adaptive thresholding and normalization, could enhance robustness and applicability. This approach lays the foundation for real-time focus optimization in underwater robotics, benefiting autonomous inspection, manipulation, and exploration.

Keywords: Multi-Robot Systems, Robot Surveillance and Security, Multisensor Data Fusion
Abstract: The Multiple Object Trajectory Estimation (MOTE) system, based on multi-hypothesis tracking (MHT), is designed to provide a unified target state estimate for a heterogeneous fleet of autonomous agents, addressing the need for real-time situational awareness and collaborative perception. By utilizing sensor fusion, our perception systems generate observations consisting of object class and 3D positions for Objects of Interest (OOI). These observations are shared between agents via radio communications and ingested by independent instances of MOTE, enabling each agent to maintain multi-target state estimates. The prototype was verified via deployment on a multi-vehicle autonomous fleet, and enabled consistent tracking for a dynamic target across varying fields of view.

Keywords: Multi-Robot Systems, Search and Rescue Robotics
Abstract: Swarm robotics offers scalable and robust solutions for tasks such as foraging, yet congestion near central collection zones remains a critical challenge, especially with increasing swarm sizes. Traditional solutions, such as static path planning or local repulsion-based methods, often fail to prevent inter-robot collisions or bottlenecks near the collection zones. This research presents a comparative study of three strategies to mitigate congestion when returning resources to the central collection zone. The research herein focuses on tightly packed environments where, in theory, robots should follow a pre-planned spiral, either square or circular, with congestion detection as described in the first two strategies. The third strategy introduces an adaptive path that allows robots to make reactive movements in response to congestion. Furthermore, we explore the use of a deep reinforcement learning (DRL) approach that trains policies in a centralized manner but executes them in a decentralized fashion, thereby preserving swarm robotic principles. Both spiral strategies integrate multiple entry points into the central collection zone and dynamic re-routing upon congestion detection. Experimental evaluation in the ARGoS physics-based simulation environment demonstrates significant improvements in task completion time, collision reduction, and system throughput. These results indicate that structured congestion-aware trajectories can significantly improve swarm foraging efficiency.

Keywords: Wheeled Mobile Robots, Multisensor Data Fusion, Mechanism and Design
Abstract: Accurate weed management is essential for mitigating significant crop yield losses, necessitating effective weed suppression strategies in agricultural systems. Integrating cover crops (CC) offers multiple benefits, including soil erosion reduction, weed suppression, decreased nitrogen requirements, and enhanced carbon sequestration, all of which are closely tied to the aboveground biomass (AGB) they produce. However, biomass production varies significantly due to microsite variability, making accurate estimation and mapping essential for identifying zones of poor weed suppression and optimizing targeted management strategies. To address this challenge, developing a comprehensive CC map, including its AGB distribution, will enable informed decision-making regarding weed control methods and optimal application rates. Manual visual inspection is impractical and labor-intensive, especially given the extensive field size and the wide diversity and variation of weed species and sizes. In this context, optical imagery and Light Detection and Ranging (LiDAR) data are two prominent sources with unique characteristics that enhance AGB estimation. This study introduces a ground robot-mounted multimodal sensor system designed for agricultural field AGB mapping. The system integrates optical and LiDAR data, leveraging machine learning methods for data fusion to improve biomass predictions. The best machine learning-based model for dry AGB estimation achieved an R^2 of 0.88, demonstrating robust performance in diverse field conditions. This approach offers valuable insights for site-specific management, enabling precise weed suppression strategies and promoting sustainable farming practices. The integration of high-resolution optical and LiDAR data from a robot-mounted system, combined with machine learning techniques, establishes a scalable framework for automated biomass estimation in large-scale agricultural field conditions, enhancing decision-making in precision agriculture.

Keywords: Robot Surveillance and Security, AI Reasoning Methods for Robotics, Industrial Robots
Abstract: Analog gauges remain widely used in industrial facilities, requiring routine manual monitoring that increases workload and exposes workers to hazardous environments. Recently, mobile robots have been increasingly deployed for automated surveillance tasks, reducing human intervention in hazardous environments. Traditional gauge reading methods rely on classical computer vision or deep learning models, but they face limitations such as sensitivity to lighting conditions and high data collection costs. To address these challenges, this study proposes a gauge reading approach utilizing a Multimodal Large Language Model (MLLM) combined with Chain-of-Thought (CoT) reasoning to improve accuracy without requiring extensive training data. Preliminary experimental results show that the CoT-based model achieves high accuracy in recognizing gauge panel elements such as unit labels and major markings but exhibits lower performance in needle position detection and final value estimation. These findings highlight both the strengths and limitations of CoT-based approaches, emphasizing the need for improved accuracy in needle position detection as a key focus for future research.

Keywords: Aerial and Flying Robots, Biomimetic and Bioinspired Robots, Mechanism and Design
Abstract: Exterior wall tasks (e.g., inspection, cleaning, and painting) are still predominantly performed manually, leading to significant risks and high costs due to the need for additional equipment. The growing construction of high-rise buildings, bridges, and irregularly shaped structures, along with the increasing use of diverse materials such as glass and metal, has further escalated the complexity and risks associated with exterior wall operations. Existing wall-climbing robots have been developed to address these challenges; however, they often rely on magnetic, vacuum, or pneumatic systems, which suffer from limitations such as material dependency, low energy efficiency, slow mobility, and difficulty in overcoming obstacles. To overcome these constraints, this paper presents a hybrid wall-climbing robot platform that integrates the rapid maneuverability of drones with the stability of six-legged walking robots. By leveraging the thrust of drone propellers and the contact forces of robotic legs, the proposed system achieves stable and energy-efficient adhesion and movement on walls, regardless of the surface material. Inspired by the perching and take-off behaviors of insects, the robot operates efficiently without the need for advanced control algorithms or high computational resources. The developed robot has been experimentally validated for its performance on walls with various materials and shapes, demonstrating key capabilities such as stable adhesion, efficient walking speed, and optimized energy consumption. Furthermore, it has been tested in real-world environments, demonstrating its potential for practical deployment. This technology is expected to significantly reduce operational costs and accident risks, providing substantial socio-economic benefits for exterior wall applications.

Keywords: Physical and Cognitive Human-Robot Interaction, Human-Robot Augmentation
Abstract: This paper presents a novel framework and describes initial progress towards designing and simulating Augmented Reality (AR) assisted Human-Robot Interaction (HRI) in industrial environments using Virtual Reality (VR). While AR has shown promise for robotic control systems, its potential benefits for non-operator stakeholders in construction and manufacturing remain largely unexplored. We propose a Virtual Reality (VR) simulation environment that enables the design and testing of AR visualizations before physical implementation. Our system provides a platform for creating context-aware AR visualizations that communicate robot intentions, such as movement previews and operational boundaries, to improve situational awareness and safety for workers in shared spaces. The framework aims to contribute to the advancement of ubiquitous robotics by bridging the gap between robotics engineers and end-users through intuitive visual communication systems.

Keywords: Dynamics and Control, Aerial and Flying Robots, Modeling, Identification, Calibration
Abstract: As the presence of autonomous drones in civilian operations continues to grow, ensuring their safe operation is crucial, as failures can lead to loss of control, system damage, property destruction, environmental harm, and even human injury. On the other hand, the inherently unstable and underactuated dynamics of quadrotors make them particularly vulnerable to system faults, especially rotor failures. In this paper, we introduce a fault-tolerant control strategy using a run time safety assurance filter based on model predictive control (MPC) to provide safety guarantees to the control input of a linear quadratic regulator (LQR) designed with the purpose of trajectory following. Our method incorporates a real-time fault detection and isolation system while backup trajectories are created for different fault modes. We demonstrate the performance of our proposed approach in a 3D simulation environment using a model of the Crazyflie 2.0 drone.

Keywords: Multisensor Data Fusion, Object Recognition, Autonoums Vehicle Navigation
Abstract: With the rapid advancement of probabilistic inference methods, diverse artificial intelligence technologies, and high-performance computing hardware, technologies related to autonomous navigation have achieved considerable development. Compared to other types of vehicles, ships operate in environments with significant variability and must sustain long-duration missions at sea. Consequently, maritime situational awareness systems for detecting objects around the vessel must demonstrate high performance and reliability, while also ensuring cost-effectiveness in terms of system development and maintenance. This paper proposes a multi-object tracking system designed for autonomous ships, taking into account their unique operational characteristics. The system is composed of multiple detection sensors and navigation sensors, and features an AI-based detection algorithm integrated with a probabilistic data fusion architecture. The structure consists of two processing stages based on the purpose of data handling, and it is designed with scalability in mind. The performance of the proposed architecture and algorithm is demonstrated through two types of experimental results. Furthermore, this paper identifies the limitations of relying solely on situational awareness systems for commercial operations and underscores the inevitability of introducing a systematic and standardized method for generating and sharing positional information in autonomous ships. Taking insights from structured environments in robotics, we suggest that this approach offers a practical pathway toward achieving both economic viability and safety, thereby accelerating the commercialization of autonomous ships given the current level of technological maturity.

Keywords: Legged Robots, World Modelling, Contact: Modeling, Sensing and Control
Abstract: We present a soft contact model to simulate diverse soft terrains, enabling robust legged locomotion through reinforcement learning. The model extends a standard spring-damper formulation with Stribeck-Coulomb friction and introduces randomized parameters, such as stiffness, damping, and friction coefficients, to capture the variability of real-world soft surfaces, including soil and mattresses. By replacing the default contact model in the simulator with our formulation, we train a locomotion policy using an existing learning framework. The resulting policy demonstrates stable walking on both flat and inclined soft terrains with the Unitree Go1 robot in simulation. Notably, it generalizes to rigid ground without explicit training, highlighting improved robustness across contact conditions. This work offers a lightweight and flexible alternative to high-fidelity contact modeling for scalable locomotion training.

Keywords: Performance Evaluation and Optimization
Abstract: This paper presents the Probability-Based Manipulability Score (PBMS), a new metric for comparing articulated manipulators with different degrees of freedom. PBMS uses a log-scaled score in a voxelized workspace to capture the effects of kinematic redundancy. This overcomes the upper-bound limitations of conventional indicators, which constrain performance index even when the degrees of freedom increase, and enables comparison across manipulators with different degrees of freedom. Simulation was performed comparing TOCABI's arm with a test manipulator in a common workspace to validate the approach. The results demonstrate that PBMS can effectively guide the task-specific design of redundant manipulators.

Keywords: Multi-Robot Systems
Abstract: This paper presents a control scheme for multirobot shepherding of non-cooperative agents, using a framework based on control barrier function and control Lyapunov function. The proposed control design guarantees the feasibility of the CLF-CBF quadratic optimization problem, even when the number of non-cooperative agents significantly exceeds the number of robots. This scheme supports distributed implementation with distributed sensing, leveraging the consensus alternating direction method of multipliers. Unlike previous works, the presented method does not impose constraints on the maximum velocities or sensing ranges of non-cooperative agents. Simulation results demonstrate that the controller can successfully shepherd large numbers of non-cooperative agents even without aggregation behaviors using teams as small as two robots.

Keywords: Multi-Robot Systems, Aerial and Flying Robots, Search and Rescue Robotics
Abstract: This paper presents high-level planning for retrieving heavy objects using an Unmanned Aerial Vehicle (UAV), a robotic manipulator, and a winch. The concept of remote grasping is introduced, where intermediate actions are required before the final grasp due to the object's initial inaccessibility. In this case, an object beyond the reach of a robotic manipulator is first grasped by a UAV and then pulled using a winch. The goal is to position the object for safe and efficient grasping by the robotic manipulator. The system is underactuated, allowing partial control over the object's position and orientation by optimizing the UAV's attaching point and the winch's tether position. The winch's tether is routed through the manipulator's end-effector, allowing its position to be adjusted by moving the manipulator. The approach was validated through simulations and laboratory experiments, demonstrating its potential for further development in autonomous retrieval systems.

Keywords: Multi-Robot Systems, Motion Planning and Obstacle Avoidance, Dynamics and Control
Abstract: We present a novel learning-based approach for dynamic obstacle avoidance in multi-robot systems using the Mori-Zwanzig (M-Z) formalism. The key innovation lies in developing a method that enables an ego robot to predict the trajectory of a dynamic obstacle that is outside its sensing range, solely by observing the historical trajectory of an ally robot. We apply M-Z formalism to rigorously justify the use of sequential data in predicting obstacle behavior, and provide theoretical bounds on the prediction accuracy. Simulation results demonstrate that our method reduces collision rates compared to scenarios without obstacle trajectory prediction.

Keywords: Multi-Robot Systems, Motion Planning and Obstacle Avoidance, Robotic Systems Architectures and Programming
Abstract: We present a dynamic multi-robot mapping framework that combines Blockchain technology for swarm management with a Hybrid Ant Colony Optimization (HACO) algorithm for path planning. Blockchain-based swarm contracts enable decentralized, transparent, and secure task allocation, acceptance, tracking, and reward distribution among multiple robots. HACO facilitates efficient path planning in complex environments through cooperative and competitive strategies. We deploy multiple LiDAR-equipped Unitree Go2 dog robots to collaboratively and competitively map divided sub-areas, with task reassignment based on real-time feedback and the selected strategy. In cooperative mode, robots share data to boost efficiency and accuracy; in competitive mode, they work independently to reduce redundancy and optimize resources. Swarm contracts also verify full sub-area coverage via the merged map. Results show that integrating blockchain-based management with HACO significantly enhances mapping performance, delivering a robust and scalable solution for real-world multi-robot systems.

Keywords: Multi-Robot Systems, Performance Evaluation and Optimization
Abstract: Over the past several decades, the number of com- mercially available robotic systems has increased significantly to meet the rising demand driven by theoretical advances and emerging practical applications. Although this growing variety offers more choices to users, it can also be overwhelming as they navigate many options to find the best system for their needs. This market saturation also forces robot providers to ensure that new robots are competitive with or superior to existing systems to increase the economic viability of their products. This need is further complicated in multi-robot applications, where understanding individual contributions to overall team performance is complex but necessary. To assist in task-driven selection and design of capable robotic systems, this paper introduces GaTORS, a novel tool that frames the robot task allocation process as a collaborative, general-sum, discrete-time game. By parameterizing robots with a set of common constraints, GaTORS enables performance evaluation of existing and hypothetical robotic systems to select teams of systems most capable of achieving a given task. We focus on robotic surface coverage applications and apply GaTORS to the problem of surface coverage for corrosion mitigation in an industrial refinery, where it is used to select a team of robots best suited to repair all identified material. We also demonstrate how GaTORS can be used to set targets for system design to create new robots that can outperform alternative systems in assigned tasks. Due to its flexibility, GaTORS can be adapted to provide similar insights for other types of robots in new environments and surface coverage applications. We release GaTORS’ code1 open-source.

Keywords: Multi-Robot Systems, Underwater Robotics
Abstract: This study presents an efficient operational framework for coverage path planning (CPP) in marine environments, utilizing multiple autonomous underwater vehicles (AUVs) deployed from a mother ship. Multi-vehicle operations in these settings encounter challenges due to energy constraints and environmental complexity. To address these, we introduce a mother ship strategy that transports AUVs to mission sites for sequential deployment. We formulate CPP as an optimization problem involving area partitioning and vehicle-to-area assignment to minimize total mission time. To tackle the complexity of coupled constraints, we develop a genetic algorithm (GA) with a two-part chromosome structure, separately encoding assignment relationships and area partitioning. Monte Carlo simulations demonstrate that our GA-based framework significantly improves operational efficiency.

Keywords: Multisensor Data Fusion, Multi-Robot Systems, Intelligent Robotic Vehicles
Abstract: Anomaly detection in autonomous driving is crucial for safety and traffic efficiency. Traditional single-vehicle systems face limitations due to restricted visibility and occlusions in complex scenarios. Cooperative Perception improves accuracy by sharing sensor data between vehicles, but challenges such as bandwidth constraints and unstable connections can lead to delayed or missing information. In this work, we present Cooperative Perception based Anomaly Detection (CPAD), a robust framework that can operate under imperfect communication. CPAD utilizes a graph-transformer architecture to model spatiotemporal correlation among vehicles and achieves a superior F1 gain of 15% and an AUC gain of 5% compared to conventional models. We also observe significant robustness against communication interruptions with an F1 gain of 19% under 25% sequential blackouts and 13% gain under 25% random blackouts compared to the best-performing model. Additionally, we present a benchmark dataset comprising 15,000 multi-agent scenarios with 90,000 vehicle trajectories. This dataset aims to address the current gap in multi-agent anomaly detection research and enhance the safety and reliability of autonomous driving systems.

Keywords: Physical and Cognitive Human-Robot Interaction, Computer Vision and Visual Servoing, Object Recognition
Abstract: Research on Intelligent Space (iSpace) has been conducted to perceive the state of a space using distributed sensors and provide appropriate services and information to users. While it is essential to create a convenient environment for users, the required functionalities of a space vary among individuals. This study proposes a system called Intelligent Space Coding (iSpace Coding), which flexibly interprets user requirements and appropriately reflects them in the space, enabling anyone to construct their own iSpace freely. Within a space defined by iSpace Coding, predefined functionalities are activated when specified conditions are met. This paper establishes the fundamental technologies of iSpace Coding by defining basic spatial functions. Users can define spatial functions by combining conditions with virtual objects and controlling home appliances through intuitive operations or natural language input. Experiments confirmed that the defined spatial functions were successfully implemented.

Keywords: Physical and Cognitive Human-Robot Interaction, Human-Robot Augmentation, Mechanism and Design
Abstract: The lightweight design of exoskeleton robots can significantly enhance wearability. This study presents AIRKNEE, an ultra-lightweight, highly compliant, and portable unilateral knee exoskeleton. Weighing less than 1.1 kg, AIRKNEE is the lightest reported portable unilateral knee exoskeleton to date, achieved by eliminating non-essential components and integrating a custom high-torque density motor with a low gear ratio (6:1) reducer. The exoskeleton exhibits exceptionally low resistance, with a backdriving torque of only 0.24 Nm when powered off. The exoskeleton provides extension torque during the loading response-to-mid-stance phase and the mid-swing phase based on a built-in accelerometer signal. To validate its effectiveness, a walking experiment was conducted with two participants wearing a 20 kg weighted vest. The results showed that, when using the exoskeleton, peak medial gastrocnemius muscle activity decreased by approximately 7.4% when walking with 20 kg load compared to walking without a load. Along with the natural improvement in wearability due to its lightweight design, the experimental results indicate that even the exoskeleton providing relatively low torque can still offer significant benefits in daily activities, such as reducing muscle activity. Future work will investigate performance across a broader range of daily activities.

Keywords: Physical and Cognitive Human-Robot Interaction, Rehabilitation and Healthcare Robotics, Soft Robotics
Abstract: Background: Functional training improves performance and injury resilience through precise motor control, yet wearable feedback systems for such training remain underdeveloped.

Objective: To develop and validate a wearable IMU-based feedback system for enhancing movement quality and muscle activation during squats and deadlifts.

Methods: A wearable system with inertial measurement units (IMUs) and vibrators was developed to monitor trunk tilt and thigh alignment. Healthy adults performed functional exercises under three conditions: No Feedback, Back Feedback, and Leg Feedback, in a randomized cross-over design. IMU and Electromyography (EMG) data were analyzed to assess movement quality and muscle activation.

Results: Among 50 participants, kinematic analysis showed that feedback led to slower but smoother and more controlled movements. Leg Feedback improved coordination and movement smoothness, while Back Feedback increased range of motion. Neuromuscularly, Back Feedback enhanced core stabilizer efficiency, whereas Leg Feedback promoted more consistent lower extremity muscle activation.

Conclusion: This study shows that real-time wearable vibrotactile feedback leads to specific biomechanical and neuromuscular adaptations, enhancing motor control and muscle efficiency, with potential for further improvement through multimodal feedback and personalized delivery.

Keywords: Physical and Cognitive Human-Robot Interaction, Social and Socially Assistive Robotics
Abstract: Most people with visual disabilities cannot use traditional assistive tools due to issues with accessibility, availability, and cost, highlighting the need for alternative in-person solutions. This work describes the conceptualization and implementation of an end-to-end Robot-Assisted Indoor Navigation (RAIN) system that can provide relevant feedback at appropriate moments to blind or low-vision (BLV) individuals as they move through indoor public spaces. Such a system needs to include multiple inter-connected modules: an Object Detection module that discerns inputs to establish the state of the world ahead while considering the user's customizable preferences, an Adaptive Navigation module that uses information from sensors to plan a path while avoiding collisions safely, and a Visual Question Answering (VQA) module that takes multimodal input (text and image), communicating with the BLV individual through a more natural interface that can process questions while maintaining alignment with the user's intent and awareness. Using this modular approach, we aim to inform both our system design and general guidelines for such assistive robot systems.

Keywords: Social and Socially Assistive Robotics, AI Reasoning Methods for Robotics, Manipulation Planning and Control
Abstract: This paper develops a Mobile Assistive Robotic System (MARS) designed to assist people with disabilities by performing daily tasks such as retrieving objects and helping meal. MARS uses ROS2 framework to connect a 7-DOF robotic arm, hybrid gripper, RGB-depth cameras, and a large language model-based planner, enabling intergration of vision-based recognition, skill affordance, and natural language task planning. Through real-world experiments in cluttered environments, MARS provides a promising solution for autonomous assistive robots in dynamic home settings

Keywords: Soft Robotics, Manipulation Planning and Control, Industrial Robots
Abstract: This work present the design and the first prototype of the novel cost-effective fiber optics sensor, FiberBend. The working principle of the proposed sensor is based on the intensity of the light transmitted through the optical fibers and detected with the camera. The measured light intensity directly correlates with the shape of the deformable soft structure, enabling simple and effective shape servoing. The linear control algorithm for the curvature angle of the soft scraper based on the FiberBend sensor measurements is implemented and deployed. Finally, the proposed FiberBend sensor is validated on the autonomous robotic plastering task.

Keywords: Soft Robotics, Modeling, Identification, Calibration, Biomimetic and Bioinspired Robots
Abstract: Soft robots, valued for their compliance and deformable nature, have demonstrated their outstanding abilities in complex environments. However, the nonlinear dynamics make it challenging to derive efficient locomotion patterns from analytical methods. This is largely due to the high computational cost associated with simulating soft-bodied models. Conversely, rigid-body models, such as those used in Gazebo, offer computational efficiency but cannot directly represent soft robots. We address these challenges by introducing customized Gazebo plugins that enable the simulation and analysis of soft robot locomotion dynamics. These plugins are complemented by a novel JointStiffnessPlugin, integrated with ROS services, for fine-tuning effort-controlled parameters. The system identification process is followed to match the simulation dynamics with the real soft robot to minimize the sim-to-real gap. Utilizing the proposed simulation framework and Bayesian Optimization, we derived a body-induced locomotion strategy that achieves enhanced efficiency. This strategy, relying solely on periodic spine bending and robot pose for forward propulsion, demonstrably reduces energy consumption compared to conventional gaits. Experimental results confirm a 42% energy expenditure reduction relative to four-legged crawling.

Keywords: Medical Robotics and Computer-Integrated Surgery, Deep Learning for Visual Percepton, Contact: Modeling, Sensing and Control
Abstract: This study presents a novel robotic framework for reconstructing 3D models of organs and lesions from sequential 2D ultrasound images using the Robotic Ultrasound Diagnostic System (RUDS). The system integrates four robotic components: the Organ Tracking Robot (OTR) for multi-angle probe control, the Phantom Posture Robot (PPR) for adaptive probe positioning, the Robotic Bed (RB) for patient alignment, and the Robotic Supporting Arm (RSA) for contact stabilization. Leveraging precise control over probe rotation and contact dynamics, the OTR captures sequential 2D slices and reconstructs detailed 3D models of the kidney and associated lesions. By dynamically adjusting scanning parameters and analyzing spatial relationships, the system enhances modeling accuracy, supporting preoperative High-Intensity Focused Ultrasound (HIFU) treatment. Experimental results demonstrate that the RUDS framework can effectively balance flexibility and precision, offering new potential for clinical ultrasound-guided robotic interventions.

Keywords: Medical Robotics and Computer-Integrated Surgery, Underwater Robotics
Abstract: Miniature Magnetic Rotating Swimmers (MMRSs) are emerging as a promising technology to improve minimally invasive vascular and cardiac surgeries. Currently, these procedures are typically performed using catheters, which are thin flexible tubes inserted into blood vessels. However, catheters rub against artery walls and can dislodge fat deposits, which can lead to complications such as stroke. In contrast, MMRS are untethered, wirelessly controlled devices actuated by an external magnetic field. Their compact size could allow them to navigate the bloodstream of a patient and reach treatment areas without the risks associated with catheter use. Surgical tasks, such as tissue cutting or ablation, require significant power, but due to their miniature size, MMRS lack the capacity to store sufficient onboard energy. This paper studies an MMRS that can be heated wirelessly via induction. The method allows transferring enough power to denature proteins.

Keywords: Micro/Nano Robots, Dynamics and Control, Medical Robotics and Computer-Integrated Surgery
Abstract: This paper presents a closed‐loop control strategy for the surface motion of magnetically actuated microswimmers, with a focus on high‐precision target reaching and smooth trajectory following. A Model Predictive Control (MPC) framework is used to control surface motion, ensuring robust tracking performance. Initially, the microswimmer is guided to a single target with high accuracy; subsequently, the controller transitions to following a spline‐based path for continuous, smooth motion. By optimizing control inputs based on predicted future states, the MPC maintains minimal tracking error. This methodology underscores the potential of surface‐based microrobotic systems for tasks requiring precise navigation, such as targeted drug delivery, micro‐scale manipulation, and other minimally invasive procedures.

Keywords: Rehabilitation and Healthcare Robotics
Abstract: Assistive exoskeletons have shown great potential in enhancing mobility for individuals with motor impairments, yet their effectiveness relies on precise parameter tuning for personalized assistance. In this study, we investigate the potential of offline reinforcement learning for optimizing effort thresholds in upper-limb assistive exoskeletons, aiming to reduce reliance on manual calibration. Specifically, we frame the problem as a multi-agent system where separate agents optimize biceps and triceps effort thresholds, enabling a more adaptive and data-driven approach to exoskeleton control. Mixed Q-Functionals (MQF) is employed to efficiently handle continuous action spaces while leveraging pre-collected data, thereby mitigating the risks associated with real-time exploration. Experiments were conducted using the MyoPro 2 exoskeleton across two distinct tasks involving horizontal and vertical arm movements. Our results indicate that the proposed approach can dynamically adjust threshold values based on learned patterns, potentially improving user interaction and control, though performance evaluation remains challenging due to dataset limitations.

Keywords: Rehabilitation and Healthcare Robotics, Medical Robotics and Computer-Integrated Surgery, Neurorobotics
Abstract: Ankle sprains are among the most common injuries in daily activities and sports. Traditional preventive measures typically rely on passive ankle straps, which can interfere with natural ankle movement. To overcome this limitation, this study introduces an ankle support controller that adjusts the joint stiffness of an active ankle device only when the ankle is at risk of injury. This approach combines signal anomaly detection with a variable stiffness control method, offering targeted protection without compromising normal gait. To develop a controller and evaluate its feasibility, ankle joint angles were measured in four healthy subjects under both normal and perturbed walking conditions. Simulation analyses were then conducted to assess the controller’s performance based on false positive and false negative rates, as well as reductions in ankle joint angle deviations from its nominal trajectory under perturbations. The results showed a 0 % false positive rate and a 4 % false negative rate (3 out of 75 trials, where the subjects quickly self-corrected their gaits), along with a 28.3 % decrease in ankle joint angle deviation. These findings indicate that the proposed controller can effectively decrease ankle joint angle deviation under perturbation without impeding natural gait, suggesting its potential to reduce the incidence of ankle sprains.

Keywords: Dynamics and Control, Aerial and Flying Robots
Abstract: This paper presents a model-based approach to attitude control for CubeSat using three orthogonally mounted reaction wheels. The dynamics of a reaction wheel-based CubeSat system are derived based on the principle of dynamical balance. All mathematical derivations start with the floating base and loading constraints, ultimately culminating in the closed-form equation of motion. The equation of motion is utilized in designing the inverse dynamics control to perform two tasks: Jumping up and Balancing.

Keywords: Underwater Robotics, Autonoums Vehicle Navigation, Deep Learning for Visual Percepton
Abstract: Underwater flow field estimation is a crucial step in marine robot missions, as the stability and safety of Autonomous Underwater Vehicles (AUVs) are heavily influenced by the ocean currents. This paper introduces a novel learning-based algorithm, called the Vision-based Motion Tomography (VMT), which enables AUVs to estimate unknown flow fields using only sporadically available visual sensor data. Simulation results show that the estimated flow map generated by the proposed algorithm converges to its corresponding ground truth.

Keywords: Underwater Robotics, Multi-Robot Systems
Abstract: This paper introduces an acoustic sensing-based speeding up and slowing down (SU-SD) algorithm for a fleet of underwater robots. Each robot transmits a series of acoustic pulses from a single transducer at a pre-defined schedule. In a bi-static setup, all robots receive acoustic signals via a hydrophone array for navigation and coordination. Robots estimate angles of arrivals from their neighbors via beamforming techniques. Ranging is achieved by measuring the one-way travel time via the matched-filtering process. Each robot also adjusts its number of transmitted pulses based on the quantization level of its measurement. Therefore, the algorithm enables decentralized operation, with robots independently analyzing acoustic signals. Velocity adjustments are made based on variations in the number of acoustic pulses detected. The swarming cohesion is maintained through relative acoustic ranging between neighboring agents. Unlike the original SU-SD implementations that rely on global information exchange, our approach uses solely acoustic sensing for both relative localization and inter-robot spacing. Computer simulations using our developed software package, muNet-AUVSim, evaluate the algorithm with detailed vehicle dynamics and hydrodynamic damping models in ocean-like conditions. A group of five autonomous underwater vehicles (AUVs) are tested in source localization of a pollution plume under varying SU-SD algorithm parameters. Results demonstrate that the swarms effectively converge on pollution sources while maintaining formation stability.

Keywords: Underwater Robotics, Physical and Cognitive Human-Robot Interaction
Abstract: Working underwater presents many challenges for human divers; however, the assistance of an underwater robot has the potential to reduce risks by collaborating with a diver and reducing the workload. This work presents a virtual reality (VR) application that simulates a BlueROV2, an underwater robot, to implement and evaluate an underwater robot-to-human interaction scenario, with the aim of improving the effectiveness of communication underwater while collaborating on an inspection and manipulation task. The use of VR allows for more repeatable assessments of robotic performance, with lower risks associated to human users, while providing a level of immersion in the task that compares to humans being underwater wearing goggles. By developing an immersive VR game, combined with Wizard-of-Oz techniques which allow for structured scenarios where the robot can be triggered to interrupt at varying points in the collaboration task, the effects of attention-grabbing strategies and the choices of signal set on human performance can be assessed, as well as the robot's ability to redirect a human collaborator in underwater tasks.

Keywords: Underwater Robotics, Range, Sonar, GPS and Inertial Sensing, Multisensor Data Fusion
Abstract: There is a consensus that utilizing simulations can reduce the costs associated with developing the sensing technology in marine robotics. Current developed marine simulators have focused on enhancing sensor simulation quality by increasing rendering quality for environment details. In this context, we developed FURo-Sim, a field and underwater robotics simulator built upon Unreal Engine 5 (UE5), which support marine sensor modules which exploit high-fidelity environments. FURo-Sim supports ROS and multiple operating systems, enabling efficient development for robotics applications. To validate sensor modules, we conducted several experiments and presented several applications in ROS.