Risk-bounded Path Planning for Urban Air Mobility Operations under Uncertainty
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Collision avoidance is an essential concern for the autonomous operations of aerial vehicles in dynamic and uncertain urban environments. This paper introduces a risk-bounded path planning algorithm for unmanned aerial vehicles (UAVs) operating in such environments. This algorithm advances the rapidly-exploring random tree (RRT) with chance constraints to generate probabilistically guaranteed collision-free paths that are robust to vehicle and environmental obstacle uncertainties. Assuming all uncertainties follow Gaussian distributions, the chance constraints are established through converting dynamic and probabilistic constraints into equivalent static and deterministic constraints. By incorporating chance constraints into the RRT algorithm, the proposed algorithm not only inherits the computational advantage of sampling-based algorithms but also guarantees a probabilistically feasible flying zone at every time step. Simulation results show the promising performance of the proposed algorithm.
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