<p>In recent years, the frequent occurrence of public health events has heightened the importance of efficient emergency material distribution. Unmanned ground vehicles, with their “contactless” distribution characteristics, have attracted increasing attention. However, significant challenges remain in dispatching trucks and UGVs efficiently to achieve material distribution. This paper addresses this gap by constructing a cooperative routing model for multiple trucks and UGVs. Given the NP-hard collaborative routing problem, an adaptive particle swarm optimization (PSO) algorithm integrating the greedy heuristic, nearest neighbor heuristic and random neighbor heuristic is proposed to improve the search efficiency and convergence stability. The experimental results show that the proposed method can provide an efficient and practical solution for emergency logistics scheduling. According to the sensitivity analysis, managers should adjust the trucks to the appropriate load levels for distribution, rather than assuming that bigger loads are always better, in order to improve overall allocation efficiency.</p>