Particle RRT for Path Planning in Very Rough Terrain

Abstract—The,Particle-based Rapidly-exploring,Random Tree (pRRT) algorithm,is a new,method,for planetary,rover path,planning,in very rough,terrain. The Rapidly-exploring Random,Tree algorithm,is a planning,technique,that accounts for effects such as vehicle dynamics,by incrementally,building a tree of reachable,states. pRRT extends,the conventional RRT algorithm by explicitly considering uncertainty in sensing, modeling, and actuation by treating each addition to the tree as a stochastic process. The pRRT algorithm has been experimentally verified in simulation, and shown to produce plans that are significantly more robust than conventional RRT. Our recent,work,has investigated several vehicle models,to improve,the performance and,accuracy,of the pRRT algorithm,in simulation. Based on these results, we have integrated the simulator with the iRobot ATRV-Jr hardware platform and tested and,verified the pRRT algorithm,using IPC communication.