Multilevel Path Planning for Nonholonomic Robots Using Semiholonomic Subsystems

We present a new and complete multi-level approach for solving path planning problems for nonholonomic robots. At the rst level a path is found that disrespects (some of) the nonholonomic constraints. At each next level a new path is generated, by transformation of the path generated at the previous level. The transformation is such that more nonholonomic constraints are respected than at the previous level. At the nal level all nonholonomic constraints are respected. We present two techniques for these transformations. The rst, which we call the Pick and Link technique, repeatedly picks pieces from the given path, and tries to replace these by more feasible ones. The second technique restricts the free con guration space to a \tube" around the given path, and a roadmap, capturing the free space connectivity within this tube, is constructed by the Probabilistic Path Planner. From this roadmap we retrieve a new, more feasible, path. In the intermediate levels we plan paths for what we refer to as semi-holonomic subsystems. Such a system is obtained by taking the real (physical) system, and removing some of its nonholonomic constraints. In this paper, we apply the scheme to car-like robots pulling trailers, that is, tractor-trailer robots. In this case, the real system is the tractor-trailer robot, and the ignored constraints in the semi-holonomic subsystems are the kinematic ones on the trailers. These are the constraints of rolling without slipping, on the trailers wheels. Experimental results are given that illustrate the time-e ciency of the resulting planner. In particular, we show that using the multi-level scheme leads to signi cantly better performance (in computation time and path shape) than direct transformations to feasible paths.