• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments Thomas M. Howard, Colin J. Green, and Alonzo Kelly Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, Pennsylvania 15213 e-mail: thoward@ri.cmu.edu, cjgreen@ri.cmu.edu, alonzo@ri.cmu.edu Dave Ferguson Intel Research Pittsburgh 4720 Forbes Avenue Pittsburgh, Pennsylvania 15213 e-mail: dave.ferguson@intel.com Received 21 December 2007; accepted 3 April 2008 Sampling in the space of controls or actions is a well-established method for ensuring fea- sible local motion plans. However, as mobile robots advance in performance and compe- tence in complex environments, this classical motion-planning technique ceases to be ef- fective. When environmental constraints severely limit the space of acceptable motions or when global motion planning expresses strong preferences, a state space sampling strat- egy is more effective. Although this has been evident for some time, the practical ques- tion is how to achieve it while also satisfying the severe constraints of vehicle dynamic feasibility. The paper presents an effective algorithm for state space sampling utilizing a model-based trajectory generation approach. This method enables high-speed navigation in highly constrained and/or partially known environments such as trails, roadways, and dense off-road obstacle fields. C  2008 Wiley Periodicals, Inc. 1. INTRODUCTION Outdoor mobile robot navigation is a challenging problem because environments are often complex and only partially known, dynamics can be difficult to predict accurately, and both planning time and computational resources are limited. We can gener- ally model the dynamics of a vehicle by a nonlinear differential equation of the form · x = f(x, u). (1) The input or control vector u and the state vector x are both time-varying points in input and state space, respectively. The complexity of such accurate mod- els of mobility combined with the scale of outdoor Journal of Field Robotics 25(6–7), 325–345 (2008) C  2008 Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com). • DOI: 10.1002/rob.20244