Adaptive Guidance Method Based on Distributed MPC of UAV Cluster for Unknown Environment Exploration

UAV cluster plays an important role in large-scale complex task, there are still many challenges like the planning and control quantities cannot be properly matched when facing the exploration of large-scale unknown environment with possible obstacles. This paper proposes an adaptive guidance distributed model predictive control (AG-DMPC) exploration method for unknown complex environment of UAV cluster. The control quantity of UAV is directly planned to avoid the conflict between planning quantity and control quantity in traditional methods. The initial solution is generated based on dynamic constraints, and the rolling optimization solution is carried out through the symbiotic biological search algorithm. The evaluation function is constructed by comprehensively considering the growth of exploration rate, cluster collision avoidance and obstacle avoidance, and the adaptive clustering guidance method is introduced to escape local maximum exploration in trap the search process, so as to realize faster exploration of the environment under the condition of ensuring safety constraints. The simulation results show that this method can effectively avoid collision problems, reduce explore lost and escape local traps to improve exploration efficiency.