Decentralized Multi-UAV Path Planning Based on Two-Layer Coordinative Framework for Formation Rendezvous

Abstract

Unmanned aerial vehicle (UAV) formation rendezvous path planning problem is one of the important research topics in multiple UAV (multi-UAV) coordinated path planning. Aiming at solving low computational efficiency and poor scalability of the traditional multi-UAV path planning method, the decentralized multi-UAV path planning method suitable for obstacle environments is proposed. Firstly, the UAV rendezvous path planning problem with constraints such as the kinematics of UAVs and collision-free constraints is modeled as a non-convex optimal control problem. To minimize formation rendezvous time and energy consumption, a two-layer coordinative framework is developed to solve this problem. In the coordination layer, relying only on the information of neighboring UAVs, each UAV in the decentralized communication graph negotiates the desired flight time using a consensus protocol to achieve coordination among UAVs. In the planning layer, the initial non-convex formation rendezvous path planning problem is decoupled into several sub-problems, which can be solved in parallel by path planners distributed on each UAV using sequential convex programming. Finally, numerical simulations are carried out to verify the effectiveness and scalability of the proposed method. The results show that this decentralized multi-UAV path planning method can handle the minimum-time rendezvous path planning problem and optimize the energy consumption in flight, and the computing time does not increase significantly with the enlargement of the UAV swarm. This decentralized framework scales well with the number of UAVs and can be applied for future urban flight and supplies delivery tasks.INDEX TERMS Unmanned aerial vehicle, path planning, decentralized coordination, sequential convex programming, collision-free.

UAV Formation Trajectory Planning Algorithms: A Review

“…Cheng et al [156] proposed a decentralized multi-UAV trajectory planning method for obstacle environments. In this method, the UAV rendezvous trajectory planning problem under constraints is modeled as a non-convex optimal control problem, and then, the consensus protocol and sequential convex programming two-layer collaborative framework are used to solve the UAV formation trajectory.…”

Cooperative Search Method for Multiple UAVs Based on Deep Reinforcement Learning

“…In [27], a fuzzy approach with a linear complexity level is used to convert the MTSP to several TSPs, then Simulated Annealing (SA) is used to solve each problem. Similarly, Cheng et al [28] decouples the MTSP problem into TSP and solves the subproblems through sequential convex programming. Reference [29] propose a task allocation algorithm based on maximum entropy principle (MEP).…”

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

“…The processing methods for geographical environments mainly include digital simulation, rasterization, and the Voronoi diagram. Based on the elevation map, researchers [1,2] converted it into a digital topographic map for path planning. The authors of [3] designed a method for the feature extraction of three-dimensional maps, which added new features to two-dimensional maps and obtained more information compared to two-dimensional maps.…”