Multi-UAV trajectory planning problem using the difference of convex function programming

Abstract

The trajectory planning problem for a swarm of multiple UAVs is known as a challenging nonconvex optimization problem, particularly due to a large number of collision avoidance constraints required for individual pairs of UAVs in the swarm. In this paper, we tackle this nonconvexity by leveraging the difference of convex function (DC) programming. We introduce the slack variables to relax and reformulate the collision avoidance conditions and employ the penalty function term to equivalently convert the problem into a DC form. Consequently, we construct a penalty DC algorithm in which we sequentially solve a set of convex optimization problems obtained by linearizing the collision avoidance constraint. The algorithm iteratively tightens the safety condition and reduces the objective cost of the planning problem and the additional penalty term. Numerical results demonstrate the effectiveness of the proposed approach in planning a large number of UAVs in congested space.