Constrained trajectory planning for unmanned aerial vehicles using asymptotic optimization approach

Abstract

Trajectory planning with the involvement of motion time has become a key and challenge for autonomous systems. This paper investigates trajectory planning of unmanned aerial vehicles (UAVs) under maneuverability and collision avoidance constraints. First, a polynomial-based trajectory planning framework is established, and a nonlinear programming problem (NLP) is formulated. Then, a novel asymptotic optimization approach is proposed to improve NLP solution success rate. Three operations of dividing the original NLP into sub-problems, adding constraints gradually, and using previous NLP solution as current initial guess value are designed in the approach. Third, an improved particle swarm optimization (PSO) path planning is also proposed to generate initial guess value for the first sub-problem. Benefited from these operations, the NLP solution success rate is significantly improved. Finally, simulations on simultaneous attack of a same target are carried out. Comparisons with other algorithms illustrate the advantage of the proposed approach.