Decentralized cooperative trajectory planning for multiple UAVs in dynamic and uncertain environments

Abstract

This paper studies the problem of planning collision-free dynamically feasible trajectories for a team of autonomous unmanned aerial vehicles (UAVs) in real-time, where the UAVs try to fly through a complex 3-D environment to reach their specified destinations. First, the cooperative trajectory planning problem is mathematically formulated as a decentralized receding horizon optimal control problem (DRH OCP). Second, a decentralized coordination strategy for multi vehicle real-time trajectory planning is designed by effectively combining the benefits of inverse dynamics optimization method and receding horizon optimal control technique. This coordination strategy mainly features that each vehicle only solves a sub-problem for its own plan and vehicles in the team make plans in parallel. Finally, simulation results show the feasibility of the proposed planning strategies.

Control design for an under-actuated UAV model

“…Zang et al (2016) address the topic of cooperative trajectory planning problem mathematically formulated as a decentralized receding horizon optimal control problem (DRH-OCP). Furthermore, the authors investigate on a decentralized coordination strategy for multi-vehicle real-time trajectory planning designed by effectively combining the benefits of inverse dynamics optimization method and receding horizon optimal control technique [24]. Trajectory control and formation control strategies are particularly important in the case of UAVs for agricultural applications and in case of surveying and measurement activities [25][26][27][28].…”

Cooperative trajectory optimization for unmanned aerial vehicles in a combat environment

“…It is a typical N-P hard optimization problem, and metaheuristic-based methods provide straightforward solutions to solve it. Zhang et al [2] proposed a hybrid optimization framework, combining an adaptive ant colony algorithm with Voronoi weighted maps, to solve the optimal trajectory in the dynamic space. Li and Chou [3] proposed a novel selfadaptive learning mechanism, incorporating adaptively local searching strategies into the framework of particle swarm optimization (PSO) to determine the optimal path for mobile robots.…”