Constraint-based semi-autonomy for unmanned ground vehicles using local sensing

Teleoperated vehicles are playing an increasingly important role in a variety of military functions. While advantageous in many respects over their manned counterparts, these vehicles also pose unique challenges when it comes to safely avoiding obstacles. Not only must operators cope with difficulties inherent to the manned driving task, but they must also perform many of the same functions with a restricted field of view, limited depth perception, potentially disorienting camera viewpoints, and significant time delays. In this work, a constraint-based method for enhancing operator performance by seamlessly coordinating human and controller commands is presented. This method uses onboard LIDAR sensing to identify environmental hazards, designs a collision-free path homotopy traversing that environment, and coordinates the control commands of a driver and an onboard controller to ensure that the vehicle trajectory remains within a safe homotopy. This system's performance is demonstrated via off-road teleoperation of a Kawasaki Mule in an open field among obstacles. In these tests, the system safely avoids collisions and maintains vehicle stability even in the presence of "routine" operator error, loss of operator attention, and complete loss of communications.

[1]  L. Evans,et al.  The dominant role of driver behavior in traffic safety. , 1996, American journal of public health.

[2]  J. Gibson,et al.  A theoretical field-analysis of automobile-driving , 1938 .

[3]  이종원,et al.  Active operator guidance using virtual environment in teleoperation , 1998 .

[4]  Martin Jägersand,et al.  Predictive display system for tele-manipulation using image-based modeling and rendering , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[5]  S. Domme,et al.  A Value Based Approach to Determining Top Hazards in Army Ground Vehicle Operations , 2006, 2006 IEEE Systems and Information Engineering Design Symposium.

[6]  Alberto Bemporad,et al.  The explicit linear quadratic regulator for constrained systems , 2003, Autom..

[7]  Raja Parasuraman,et al.  Human-Automation Interaction , 2005 .

[8]  Sterling J. Anderson,et al.  An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios , 2010 .

[9]  Robin R. Murphy,et al.  How UGVs physically fail in the field , 2005, IEEE Transactions on Robotics.

[10]  S S Stevens,et al.  HUMAN ENGINEERING FOR AN EFFECTIVE AIR-NAVIGATION AND TRAFFIC-CONTROL SYSTEM, AND APPENDIXES 1 THRU 3 , 1951 .

[11]  Robert Meyers,et al.  Real-time photorealistic virtualized reality interface for remote mobile robot control , 2010, ISRR.

[12]  Luke Fletcher,et al.  A perception-driven autonomous urban vehicle , 2008 .

[13]  S. Kicha Ganapathy,et al.  A synthetic visual environment with hand gesturing and voice input , 1989, CHI '89.

[14]  Myung Jin Chung,et al.  Intelligent motion control strategy for a mobile robot in the presence of moving obstacles , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[15]  Joel W. Burdick,et al.  Alice: An information‐rich autonomous vehicle for high‐speed desert navigation , 2006 .

[16]  Stefano Caselli,et al.  Multimodal user interface for remote object exploration with sparse sensory data , 2002, Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication.

[17]  Roger D. Quinn,et al.  Evolutionary path planning for autonomous air vehicles using multi-resolution path representation , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).