Motion interference detection in mobile robots

As mobile robots become better equipped to autonomously navigate in human-populated environments, they need to become able to recognize internal and external factors that may interfere with successful motion execution. Even when these robots are equipped with appropriate obstacle avoidance algorithms, collisions and other forms of motion interference might be inevitable: there may be obstacles in the environment that are invisible to the robot's sensors, or there may be people who could interfere with the robot's motion. We present a Hidden Markov Model-based model for detecting such events in mobile robots that do not include special sensors for specific motion interference. We identify the robot observable sensory data and model the states of the robot. Our algorithm is motivated and implemented on an omnidirectional mobile service robot equipped with a depth-camera. Our experiments show that our algorithm can detect over 90% of motion interference events while avoiding false positive detections.

[1]  Honghai Liu,et al.  A model-based approach to robot fault diagnosis , 2004, Knowl. Based Syst..

[2]  Alessandro Saffiotti,et al.  Monitoring the execution of robot plans using semantic knowledge , 2008, Robotics Auton. Syst..

[3]  Ola Pettersson,et al.  Execution monitoring in robotics: A survey , 2005, Robotics Auton. Syst..

[4]  Richard Washington,et al.  On-board real-time state and fault identification for rovers , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[5]  Alessandro Saffiotti,et al.  Model-Free Execution Monitoring in Behavior-Based Robotics , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Reid G. Simmons,et al.  Robust execution monitoring for navigation plans , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[7]  Stephanie Rosenthal,et al.  Symbiotic-Autonomous Service Robots for User-Requested Tasks in a Multi-Floor Building , 2012, IROS 2012.

[8]  S. Thrun,et al.  Particle Filters for Rover Fault Diagnosis , 2004 .

[9]  Hugh F. Durrant-Whyte,et al.  Frequency domain modeling of aided GPS for vehicle navigation systems , 1998, Robotics Auton. Syst..

[10]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[11]  M. Veloso,et al.  Learning and Recognizing Activities in Streams of Video , 2005 .

[12]  Manuela Veloso,et al.  Automated Robot Behavior Recognition , 2000 .

[13]  Hugh F. Durrant-Whyte,et al.  The detection of faults in navigation systems: a frequency domain approach , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).