Enhancing sampling-based kinodynamic motion planning for quadrotors

Abstract

The overall performance of sampling-based motion planning algorithms strongly depends on the use of suitable sampling and connection strategies, as well as on the accuracy of the distance metric considered to select neighbor states. Defining appropriate strategies and metrics is particularly hard when considering robot dynamics, which is required to treat constrained motion planning problems for quadrotors. This paper presents an accurate but computationally fast quasi-metric to determine the proximity of dynamic states of a quadrotor, and an incremental state-space sampling technique to avoid generating local trajectories that violate kinodynamic constraints. Results show that the integration of the proposed techniques in RRT-based and PRM-based algorithms can drastically decrease computing time, up to two orders of magnitude.