Event-Triggered Collision-Avoidance Flocking Control for Multi-Agent Systems with Collision Prediction

Flocking control for multi-agent systems exhibits promising application prospects in numerous areas including cooperative target search, environmental monitoring, disaster rescue, and intelligent transportation. To solve the problem of collision-avoidance flocking control for multi-agent systems in obstacle environments, this paper presents an event-triggered flocking control scheme based on collision prediction. Firstly, a finite-horizon collision prediction method is adopted. Future motion trajectories are predicted based on the current velocity and control inputs to assess the collision risk for each candidate strategy. The optimal strategy that balances obstacle avoidance, flocking performance and virtual leader tracking performance is selected. Secondly, aiming at the issue that continuous control updates easily lead to the waste of computational and communication resources, a hybrid event-triggered mechanism combining error threshold triggering and environment change triggering is developed. The closed-loop system is proven to be globally uniformly ultimately bounded. Simulation results show that the proposed method achieves better control performance than existing methods in various complex environments.