Probabilistic Guaranteed Path Planning for Safe Urban Air Mobility Using Chance Constrained RRT

TL;DR

This paper introduces a probabilistic path planning algorithm for urban air mobility that ensures safety by incorporating chance constraints into RRT, effectively handling uncertain obstacles in dynamic environments.

Contribution

It develops a novel chance-constrained RRT algorithm that converts probabilistic obstacle constraints into deterministic ones for Gaussian uncertainties, enhancing safety in urban air mobility planning.

Findings

Successfully plans safe trajectories avoiding uncertain obstacles

Demonstrates effectiveness through simulation results

Integrates uncertainty into sampling-based path planning

Abstract

Safety is a critical concern for the success of urban air mobility, especially in dynamic and uncertain environments. This paper proposes a path planning algorithm based on RRT in conjunction with chance constraints in the presence of uncertain obstacles. The chance-constrained formulation for Gaussian distributed obstacles is developed by converting the probabilistic constraints to deterministic constraints in terms of distribution parameters. The probabilistic feasible region at every time step can be established through the simulation of the system state and the evaluation of convex constraints. Through establishing chance-constrained RRT, the algorithm not only enjoys the benefits of sampling-based algorithms but also incorporates uncertainty into the formulation. Simulation results demonstrate that the planning for a trajectory connecting the starting and goal point in accordance with the requirement of probabilistic obstacle avoidance can be achieved by the utilization of this algorithm.