Collaborative control method for multi-UAV search trajectory in high-rise building emergency rescue

To address the issues of low learning efficiency and poor strategy robustness in multi-UAV systems during their collaborative emergency search task for high-rise buildings, caused by insufficient experience in close-range inter-agent collision and formation reconfiguration, a multi-agent deep deterministic policy gradient model integrated with prioritized experience replay (PER-MADDPG) was proposed. A UAV swarm simulation environment with a six-DOF dynamic model was established. The multi-UAV collaborative search task was formulated as a multi-agent Markov decision process, and a hierarchical reward function integrating individual trajectory tracking, energy constraints, team formation keeping, and collision avoidance requirements was designed. A centralized critic network was used to calculate the temporal difference errors of the team's joint actions. The joint experiences were quantified, and the prioritized sampling was implemented. The algorithm was guided to focus on high-value sparse collaborative samples. As a result, the convergence to robust collaborative policies was accelerated. Experimental results show that a 98% task success rate is achieved by the PER-MADDPG algorithm, 15.3% higher than the baseline MADDPG algorithm, and the inter-agent collision rate is reduced from 8% to 1%. In terms of collaboration and control accuracy, the average formation error is decreased from 0.07 m to 0.03 m, and the average trajectory tracking error is lowered from 0.12 m to 0.05 m. In the scalability tests on four-UAV and six-UAV formations, the performance degradation caused by physical space congestion is effectively overcome, demonstrating superior robustness to baseline algorithms. The established PER-MADDPG can effectively balance individual control accuracy and team collaboration stability, enhancing search efficiency in high-rise building emergency rescue.