Fast Generation of Chance-Constrained Flight Trajectory for Unmanned Vehicles

In this article, a fast chance-constrained trajectory generation strategy incorporating convex optimization and approximation of chance constraints is designed so as to solve the unmanned vehicle path planning problem. A path-length-optimal model constructed with consideration pitch angle constraint, curvature radius probabilistic control actuation collision avoidance constraint. Subsequently, convexification technique introduced convert nonlinear problem formulation into form. To deal in model, techniques are such that replaced by deterministic ones while simultaneously preserving convexity model. Numerical results, obtained from number case studies, validate effectiveness reliability proposed approach. comparative studies were also performed. The results confirm design able produce more optimal flight paths achieve enhanced computational performance than other approaches investigated article.