Optimization-Based Navigation for the DARPA Grand Challenge

This research addresses the path planning problem with a nonlinear optimization method running in real time. An optimization problem is continually solved to find a time-optimal, dynamically feasible trajectory from the vehicle’s position to some receding horizon ahead (20m-70m forward). The locally optimal numerical solver optimizes both the spatial and temporal components of the trajectory simultaneously, and feeds its output to a trajectory-following controller. The method has been implemented and tested on a modified Ford E350 van. Using one stereo pair and four LADAR units as terrain sensors, the vehicle was able to consistently traverse a 2 mile obstacle course at the DGC qualifying event. At the main DGC event, the vehicle drove 8 autonomous miles through the Nevada desert before experiencing non-planning issues. During this time, the planning system generated a plan 4.28 times per second on average. This execution speed, coupled with a feedback-based trajectory-following controller was shown to be adequate at providing smooth and reliable obstacle avoidance even on complicated terrain.

[1]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Zvi Shiller,et al.  Dynamic motion planning of autonomous vehicles , 1991, IEEE Trans. Robotics Autom..

[3]  M. Fliess,et al.  On Differentially Flat Nonlinear Systems , 1992 .

[4]  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.

[5]  Richard M. Murray,et al.  A testbed for nonlinear flight control techniques: the Caltech ducted fan , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[6]  Peng-Yung Woo,et al.  Time optimal path planning for a wheeled mobile robot , 2000, J. Field Robotics.

[7]  Karl Murphy,et al.  Driving autonomously off-road up to 35 km/h , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[8]  Michael A. Saunders,et al.  USER’S GUIDE FOR SNOPT 5.3: A FORTRAN PACKAGE FOR LARGE-SCALE NONLINEAR PROGRAMMING , 2002 .

[9]  Antonio Simón Mata,et al.  Optimal Velocity Planning of Wheeled Mobile Robots on Specific Paths in Static and Dynamic Environments , 2003, J. Field Robotics.

[10]  Mark B. Milam,et al.  Real-Time Optimal Trajectory Generation for Constrained Dynamical Systems , 2003 .

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

[12]  G. Swaminathan.  Robot Motion Planning , 2006 .