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Paper ThP202.6

Ding, Hao (University of Kassel), Reissig, Gunther (University of Kassel), Wijaya, Kurniawan (Technische Universitaet Muenchen), Bortot, Dino (Technische Universitaet Muenchen), Bengler, Klaus (Technische Universitaet Muenchen), Stursberg, Olaf (University of Kassel)

Human Arm Motion Modeling and Long-Term Prediction for Safe and Efficient Human-Robot-Interaction

Scheduled for presentation during the Regular Sessions "Robot Design for Advanced Applications" (ThP202), Thursday, May 12, 2011, 16:40−16:55, Room 3C

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on August 18, 2019

Keywords Cognitive Human-Robot Interaction, Physical Human-Robot Interaction, Recognition

Abstract

Modeling and predicting human behavior is indispensable when industrial robots interacting with human operators are to be manipulated safely and efficiently. One challenge is that human operators tend to follow different motion patterns, depending on their intention and the structure of the environment. This precludes the use of classical estimation techniques based on kinematic or dynamic models, especially for the purpose of long-term prediction. In this paper, we propose a method based on Hidden Markov Models to predict the region of the workspace that is possibly occupied by the human within a prediction horizon. In contrast to predictions in the form of single points such as most likely human positions as obtained from previous approaches, the regions obtained here may serve as safety constraints when the robot motion is planned or optimized. This way one avoids collisions with a probability not less than a predefined threshold. The practicability of our method is demonstrated by successfully and accurately predicting the motion of a human arm in two scenarios involving multiple motion patterns.

 

 

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