UAV Trajectory Planning With Probabilistic Geo-Fence via Iterative Chance-Constrained Optimization

Chance-constrained optimization provides a promi-sing framework for solving control and planning problems with uncertainties, due to its modeling capability to capture randomness in real-world applications. In this paper, we consider a UAV trajectory planning problem with probabilistic geo-fence, building on the chance-constrained optimization approach. In the considered problem, randomness of the model, such as the uncertain boundaries of geo-fences, is incorporated in the formulation. By solving the formulated chance-constrained optimization with a novel sampling based solution method, the optimal UAV trajectory is achieved while limiting the probability of collision with geo-fences to a prefixed threshold. Furthermore, to obtain a totally collision-free trajectory, i.e., avoiding the collision not only at the discrete time-steps but also within the entire time horizon, we build on the idea of an iterative scheme. That is, to iterate the solving of the chance-constrained optimization until the collision with probabilistic geo-fence is avoided at any time within the time horizon. At last, we validate the effectiveness of our method via numerical simulations.

Automated summary

In this paper, chance-constrained optimization is used to solve a UAV trajectory planning problem with probabilistic geo-fence, achieving the optimal trajectory while limiting collision probability. An iterative scheme is employed to ensure total collision-free trajectory over the entire time horizon, validated through numerical simulations.