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

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