Optimization of Robot Paths Computed by Randomized Planners

Randomized path planners have been successfully used to compute feasible paths for difficult planning problems. Such paths are typically computed without taking into account any optimality criteria and may contain many “jagged” segments because of the randomness involved in their generation. This paper presents a two-phase path planning algorithm, which uses a randomized planner to compute low-cost paths, and gradient descent to locally optimize these paths by minimizing a Hamiltonian function. The algorithm is tested on motion planning for a non-holonomic car-like robot. The results indicate that the two-phase approach is practical; however, gradient descent seems to be inefficient for the optimization of long paths.