A Planning System for Autonomous Ground Vehicles Operating in Unstructured Dynamic Environments

This paper describes the design and implementation of a path planning system for an autonomous ground vehicle. The system is designed to be flexible, allowing any planning algorithm to be used and any topology of data to be planned over. It employs a hierarchical separation of two planning modules in conjunction with a vehicle model, to achieve continued vehicle motion while planning and the ability to act as either a deliberative or reactive planner, or a hybrid of both types. Results from both simulation and field trials are presented, and demonstrate the effectiveness of this architecture on a large outdoor ground vehicle. The contributions of this paper are twofold: a flexible planning system capable of large scale missions for autonomous vehicles; and the use of a vehicle model to determine the requirements for safe operation without slowing the vehicle, and the conditions under which this cannot be achieved.

[1]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[2]  Verzekeren Naar Sparen,et al.  Cambridge , 1969, Humphrey Burton: In My Own Time.

[3]  Alexander Zelinsky,et al.  A mobile robot exploration algorithm , 1992, IEEE Trans. Robotics Autom..

[4]  Anthony Stentz.  Optimal and Efficient Path Planning for Unknown and Dynamic Environments , 1993 .

[5]  Anthony Stentz,et al.  Optimal and efficient path planning for partially-known environments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[6]  Swaminathan Natarajan.  Imprecise and Approximate Computation , 1995 .

[7]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[8]  Anthony Stentz,et al.  The Focussed D* Algorithm for Real-Time Replanning , 1995, IJCAI.

[9]  Martial Hebert,et al.  A complete navigation system for goal acquisition in unknown environments , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[10]  Barry Brumitt,et al.  Framed-quadtree path planning for mobile robots operating in sparse environments , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[11]  Sven Koenig,et al.  Improved fast replanning for robot navigation in unknown terrain , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[12]  Sebastian Thrun,et al.  ARA*: Anytime A* with Provable Bounds on Sub-Optimality , 2003, NIPS.

[13]  P. Tompkins,et al.  Global path planning for Mars rover exploration , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[14]  Alonzo Kelly,et al.  Efficient Constrained Path Planning via Search in State Lattices , 2005 .

[15]  Sebastian Thrun,et al.  Anytime Dynamic A*: An Anytime, Replanning Algorithm , 2005, ICAPS.

[16]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[17]  Anthony Stentz,et al.  Using interpolation to improve path planning: The Field D* algorithm , 2006, J. Field Robotics.

[18]  Ross A. Knepper,et al.  High Performance State Lattice Planning Using Heuristic Look-Up Tables , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  A. Stentz,et al.  Global Path Planning on Board the Mars Exploration Rovers , 2007, 2007 IEEE Aerospace Conference.