Keywords: Robot communications, Rescue robotics, Mobile Robotics
Abstract: Wireless communication technologies are designed to cover specific scopes of use cases and, therefore, possess strengths and weaknesses inherent to their designated application area. As a critical enabler for robotic remote operations, wireless communications are expected to perform optimally, sometimes even in situations outside the respective technology’s intended deployment scope. Since a single technology can hardly ever meet the high requirements, various approaches to aggregate multiple communication links, so-called multi-links, have emerged in recent years. In this paper, we propose the novel open-source multi-link solution SEAMLESS to provide reliable connectivity in the context of rescue robotics in search and rescue missions. It improves flexibility by supporting general internet protocol service tunneling and multiple schedulers. As wireless technologies can not be assessed solely on network key performance indicators, an open radio monitoring interface is implemented, allowing radio metric aware scheduling. A comprehensive evaluation is carried out in two experiments, in both indoor and outdoor testing sites. The results showcase the benefits of the proposed radio metric multi-link scheduling by demonstrating a reliable high-resolution video transmission in challenging radio environments over Wi-Fi 6 and public cellular 5G.

Keywords: Human-robot interaction, Rescue robotics, Safety standards for robots and systems
Abstract: Just like with crewed aviation, fatigue is a real problem for sUAS pilots. However, unlike crewed aviation, there are no regulations and few explicit guidelines available for mitigating fatigue when piloting sUAS’. This is particularly relevant for piloting sUAS’ during disaster response. This paper presents a framework of aspects that contribute to human performance and fatigue and some best practice for mitigating that fatigue before and during disaster response.

Keywords: Rescue robotics, Field robotics, Human-robot interaction
Abstract: This paper provides a summary of the 281 sorties that were flown by the 10 different models of small unmanned aerial systems (sUAS) at Hurricane Ian, and the failures made in the field. These 281 sorties, supporting 44 missions, represents the largest use of sUAS in a disaster to date (previously Hurricane Florence with 260 sorties). The sUAS operations at Hurricane Ian differ slightly from prior operations as they included the first documented uses of drones performing interior search for victims, and the first use of a VTOL fixed wing aircraft during a large scale disaster. However, there are substantive similarities to prior drone operations. Most notably, rotorcraft continue to perform the vast majority of flights, wireless data transmission capacity continues to be a limitation, and the lack of centralized control for unmanned and manned aerial systems continues to cause operational friction. This work continues by documenting the failures, both human and technological made in the field and concludes with a discussion summarizing potential areas for further work to improve sUAS response to large scale disasters.

Keywords: Rescue robotics, Aerial robotics, Artificial Intelligence
Abstract: In the realm of digital situational awareness during disaster situations, accurate digital representations, like 3D models, play an indispensable role. To ensure the safety of rescue teams, robotic platforms are often deployed to generate these models. In this paper, we introduce an innovative approach that synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller than 30 cm, equipped with 360° cameras and the advances of Neural Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D representation of any scene using 2D images and then synthesize it from various angles upon request. This method is especially tailored for urban environments which have experienced significant destruction, where the structural integrity of buildings is compromised to the point of barring entry—commonly observed post-earthquakes and after severe fires. We have tested our approach through recent post-fire scenario, underlining the efficacy of NeRFs even in challenging outdoor environments characterized by water, snow, varying light conditions, and reflective surfaces.

Keywords: Structural assessment
Abstract: In this study, we analyzed the behavior of a gantry crane (hereafter referred to as crane) that started to runaway owing to an unforeseen strong wind, operated the rail brakes, and then decelerated and stopped. In the analysis, the cranes in cases wherein the cabin is at the standby position and at the seaside limit position of traverse were targeted. When the rail brakes started to operate, the acceleration and displacement of the cabin at the seaside limit position of traverse were larger than those at the standby position. After the crane runaway ended, the cabin had a residual swing. The frequency of the residual swing of the cabin at the seaside limit position of traverse was smaller than that at the standby position. Because the displacement and acceleration of crane swing that an operator is exposed to become large when the cabin is at the seaside limit position of traverse, it is desirable to return the cabin to the standby position in a situation wherein the rail brakes may be used.

Keywords: Rescue robotics, Perception for navigation, hazard detection, and victim identification, Mechanisms, Mechatronics, and Embedded Control
Abstract: In mobile robotics, many kinds of sensors and systems are in use. These are oftentimes capable of functioning independently of the actual robot functions. Therefore, in most cases they are applicable to be carried by many mobile robot platforms. However, in common practice, the systems are not build in an inter-robot-transferable way and hence robots are custom-built for specific tasks and are costly to modify. This results in immense potential and time loss, when payloads are to be transferred to other robots. One goal of the German Center for Rescue Robotics, is to establish a concept that enables the exchange of self-contained payload modules to adapt to multiple use cases. This paper proposes a new system architecture for suitable hardware and software components that enable the exchange of specialized payload modules between multiple robot platforms via standardized interfaces. Exemplary functions of payload modules, that are compatible with the used concept, include but are not limited to fire detection and fire extinguishing. Using the example of a human detection and vital sign measurement module, the concept's potential is demonstrated. Our results show that such payload modules can be developed and tested separately, without the need for a robot, as the standardized interface allows for easy integration with multiple robot platforms. This approach reduces the overall development time and required expertise for adding new functionalities to a robot system, as it eliminates the need for extended knowledge in robotics. Instead, expertise in the specific application domain is sufficient. Moreover, the developed modular robot concept, allows for quick and easy reconfiguration of a robot's capabilities through standardized interfaces.

Keywords: Rescue robotics, Field robotics, Multi-robot systems
Abstract: Bringing autonomous systems to the field is a challenging task, especially in the context of disaster response where requirements in terms of reliability, usability, and performance are very high. However, multiple scenarios would benefit from robotic assistance and partial automation, especially in the context of firefighting in challenging environments such as mountainous areas. We aim at advancing towards such practical deployments, by presenting a system architecture for a support system designed to help in those situations based on results from workshops performed with firefighters to specify a relevant use-case and requirements. Furthermore, we present an implementation and its testing during a realistic field experiment in mountainous terrain, where UAVs and UGVs work in cooperation to support first responders in addressing wildfires in challenging environments. Finally, we present the results of a usability survey conducted with the firefighters present during the experiment, showing the potential of such a system for supporting firefighting and gaining insights on factors such as reliability, controllability, and safety that are important for user acceptance.

Keywords: Detection and mitigation of chemical, biological, radiological, nuclear, and explosive (CBRNE) events, Rescue robotics, Perception for navigation, hazard detection, and victim identification
Abstract: We present a novel method for online radiation mapping and source localization in 2D and 3D with mobile ground robots using Gaussian Processes to assist personnel in potentially dangerous scenarios such as nuclear catastrophes or dismantling nuclear reactors. While existing methods typically make strong model assumptions or are limited for robot onboard application by high computational cost, we propose a method that requires only weak model assumptions and gains efficiency by pre-sampling and local map update schemes. The resulting models can predict the radiation levels in complex indoor environments with multiple sources and quantify the uncertainty in their estimates.

The proposed method can be applied in combination with teleoperated, semi-autonomous, or autonomous exploration. It was successfully evaluated at the EnRicH 2023 competition in a decommissioned nuclear power plant, where it provided the best localization and mapping of five radiation sources and received the award for radiation mapping. Our evaluation of data from the competition validates the accuracy and computational efficiency of the proposed approach. Moreover, we provide an open-source ROS implementation of the proposed method and open-access evaluation data.

Keywords: Planning, reasoning, and modeling, Mobile Robotics, Rescue robotics
Abstract: Autonomous locomotion for mobile ground robots in unstructured environments such as waypoint navigation or flipper control requires a sufficiently accurate prediction of the robot-terrain interaction. Heuristics like occupancy grids or traversability maps are widely used but limit actions available to robots with active flippers as joint positions are not taken into account. We present a novel iterative geometric method to predict the 3D pose of mobile ground robots with active flippers on uneven ground with high accuracy and online planning capabilities. This is achieved by utilizing the ability of signed distance fields to represent surfaces with sub-voxel accuracy. The effectiveness of the presented approach is demonstrated on two different tracked robots in simulation and on a real platform. Compared to a tracking system as ground truth, our method predicts the robot position and orientation with an average accuracy of 3.11 cm and 3.91°, outperforming a recent heightmap-based approach. The implementation is made available as an open-source ROS package.

Keywords: Autonomous search and rescue, Perception for navigation, hazard detection, and victim identification, Human-robot interaction
Abstract: Autonomous and tele-operation of rescue robots in urban search and rescue (USAR) environments is very challenging as details of missions and environments are usually unknown, mission goals might change dynamically and there is only little repeatability between different missions. There- fore, we propose a novel actionable semantic mapping and planning approach which leverages complementary capabilities of operator and robotic assistance functions. While related methods often focus on accuracy for geometric or semantic representations, we propose a novel framework focusing on an actionable map representation which is well suited for planning complex behaviors in uncertain environments. We represent the environment topologically as a scene graph coupled with a geometrically and semantically dense representation as Truncated Signed Distance Functions. We propose to apply the concept of affordances to map possible actions and costs to object classes. Building on that, we propose a combined topological and geometric task planning method allowing for easy operator interaction on task selection and prioritization. The successful application in two complex scenarios demonstrates the flexibility and efficiency of the proposed approach and the benefit of operator interaction.

Keywords: Localization, Mapping, and Navigation, Aerial robotics, Sensing and sensor fusion
Abstract: In this paper, we present a Radar-Inertial Odometry (RIO) framework capable of running on a portable resource-constrained embedded computer in real-time as a state estimator for closing the feedback control loop on an Unmanned Aerial Vehicle (UAV) platform. The presented framework efficiently implements a RIO approach relying on the multi-state tightly-coupled Extended Kalman Filter (EKF) fusing instantaneous velocities of and distances to 3D points delivered by a lightweight, low-cost, off-the-shelf Frequency Modulated Continuous Wave (FMCW) radar with Inertial Measurement Unit (IMU) readings. The usage, accuracy and consistency of the implemented framework are improved compared to state-of-the-art by the online calibration of the sensors’ extrinsic parameters. Our method is particularly relevant for (but not limited to) UAVs, enabling them to navigate autonomously in Global Navigation Satellite System (GNSS)-denied environments using very affordable and accessible hardware. In addition, thanks to the properties of the radar sensor, we enable autonomous navigation in challenging conditions for robot perception due to external factors such as fog, darkness or strong illumination which might be encountered in disaster zones. We show in real-world closed-loop flight experiments the effectiveness and efficiency of our estimator. The beneficial impact of the online calibration on estimation accuracy and consistency is also shown. Moreover, we compare the presented approach to a state-of-the-art vision-based algorithm (Visual-Inertial Odometry (VIO)) in visually degraded conditions.

Keywords: Localization, Mapping, and Navigation, Inspection of critical infrastructure, Field robotics
Abstract: Autonomous inspection of dark, confined, and feature-poor spaces requires robotic platforms to utilize accurate and reliable localization systems for safe and reliable operation. This paper presents an absolute localization system for highly feature-poor spaces, using visual inertial odometry and GPU-based point cloud registrations for limited field-of-view sensors. The extracted structural elements from sensor scans, along side IMU measurements, are used to limit the search area for the GPU-based point cloud registrations. We employ Stein-ICP which is an uncertainty aware variant of ICP. The 3D registrations are then fused with a visual-inertial odometry estimate in an Extended Kalman Filter to provide a fast and accurate absolute pose estimate. The proposed localization system is tested in both a simulated environment and in a mock-up model of a chemical distillation column — both highly feature-poor areas.