Probabilistically Guaranteed Path Planning for Safe Urban Air Mobility Using Chance Constrained RRT*
Pengcheng Wu, Lin Li, Junfei Xie, Jun Chen · AIAA AVIATION 2020 FORUM · 2020
Flying safety is a critical concern for the successful operation of urban air mobility. This paper proposes a novel path planning algorithm based on the Rapidly-exploring Random Tree Star (RRT*) in conjunction with chance constrained formulation to handle uncertain environmental obstacles. Chance constrained formulation for uncertain obstacles under Gaussian noise is developed by converting the probabilistic constraints into deterministic constraints equivalently. The probabilistically feasible region at every time step can be established through the simulation of the system state and the evaluation of probabilistic constraints. By combining chance constrained formulation with RRT* algorithm, our proposed strategy not only enjoys the benefits of sampling-based algorithms but also incorporates uncertainty into the formulation. Simulation results demonstrate that the proposed algorithm can generate probabilistically guaranteed collision-free paths for urban air mobility operations.