Learning obstacle avoidance parameters from operator behavior

This paper concerns an outdoor mobile robot that learns to avoid collisions by observing a human driver operate a vehicle equipped with sensors that continuously produce a map of the local environment. We have implemented steering control that models human behavior in trying to avoid obstacles while trying to follow a desired path. Here we present the formulation for this control system and its independent parameters and then show how these parameters can be automatically estimated by observing a human driver. We also present results from operation on an autonomous robot as well as in simulation, and compare the results from our method to another commonly used learning method. We find that the proposed method generalizes well and is capable of learning from a small number of samples. © 2007 Wiley Periodicals, Inc.

[1]  Robert B. Tilove,et al.  Local obstacle avoidance for mobile robots based on the method of artificial potentials , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[2]  Wendelin Feiten,et al.  Robust obstacle avoidance in unknown and cramped environments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[3]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[4]  Charles E. Thorpe,et al.  ELVIS: Eigenvectors for Land Vehicle Image System , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[5]  Reid G. Simmons,et al.  The curvature-velocity method for local obstacle avoidance , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[6]  Dean A. Pomerleau,et al.  Neural Network Vision for Robot Driving , 1997 .

[7]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[8]  Iwan Ulrich,et al.  VFH/sup */: local obstacle avoidance with look-ahead verification , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Roberto Manduchi,et al.  Terrain perception for DEMO III , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[10]  Reid G. Simmons,et al.  Recent progress in local and global traversability for planetary rovers , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[11]  Ieee Robotics.  Proceedings, 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 30-October 4, EPFL Lausanne, Switzerland , 2002 .

[12]  Oliver Brock,et al.  Elastic Strips: A Framework for Motion Generation in Human Environments , 2002, Int. J. Robotics Res..

[13]  Roberto Manduchi,et al.  Autonomous terrain characterisation and modelling for dynamic control of unmanned vehicles , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Reid G. Simmons,et al.  Stereo vision based navigation for Sun-synchronous exploration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  S. Shankar Sastry,et al.  Autonomous Helicopter Flight via Reinforcement Learning , 2003, NIPS.

[16]  Brett R Fajen,et al.  Behavioral dynamics of steering, obstacle avoidance, and route selection. , 2003, Journal of experimental psychology. Human perception and performance.

[17]  O. Brock,et al.  Elastic Strips: A Framework for Motion Generation in Human Environments , 2002, Int. J. Robotics Res..

[18]  Anthony Stentz,et al.  Learning Predictions of the Load-Bearing Surface for Autonomous Rough-Terrain Navigation in Vegetation , 2003, FSR.

[19]  Claire D'Este,et al.  Behavioural Cloning and Robot Control , 2003, Robotics and Applications.

[20]  Ben Tse,et al.  Autonomous Inverted Helicopter Flight via Reinforcement Learning , 2004, ISER.

[21]  Leslie Pack Kaelbling,et al.  A Dynamical Model of Visually-Guided Steering, Obstacle Avoidance, and Route Selection , 2003, International Journal of Computer Vision.

[22]  Sebastian Thrun,et al.  Bayesian Landmark Learning for Mobile Robot Localization , 1998, Machine Learning.

[23]  Florent Lamiraux,et al.  Reactive path deformation for nonholonomic mobile robots , 2004, IEEE Transactions on Robotics.

[24]  Juan Pablo Gonzalez,et al.  High Speed Navigation of Unrehearsed Terrain: Red Team Technology for Grand Challenge 2004 , 2004 .

[25]  Javier Minguez,et al.  Nearness diagram (ND) navigation: collision avoidance in troublesome scenarios , 2004, IEEE Transactions on Robotics and Automation.

[26]  Amos Storkey,et al.  Advances in Neural Information Processing Systems 20 , 2007 .

[27]  Roland Siegwart,et al.  An Interpolated Dynamic Navigation Function , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[28]  Myung Hwangbo,et al.  Results in Combined Route Traversal and Collision Avoidance , 2005, FSR.

[29]  Brett R. Fajen,et al.  Visual navigation and obstacle avoidance using a steering potential function , 2006, Robotics Auton. Syst..