Multi-Agent Collision Avoidance System Based on Centralization and Decentralization Control for UAV Applications

Abstract

A collision avoidance method for multi-agent systems based on the centralization and decentralization effects for cooperative control is presented in this article. In this context, a matrix called Anti-Laplacian is proposed to control the agents when the UAVs are on a conflicting route. The matrix adjustment occurs through proportionality relations with the Laplacian Matrix from an interaction graph between the agents. The adjustment method aims a balance between centering and decentering to avoid collisions. Controlled quadcopters follow the trajectory indicated by virtual agents that act as guides for the real ones. The tests are performed via simulation for the most critical cases, with protocols involving flock centralization. As a virtual agent, a first-order model is used in the simulation, the method efficiency is observed by varying the number of agents involved. The proposed method presents allows different forms to control the collision avoidance parameter, such as deterministic and machine learning methods.