Cooperative UAV Swarm Data Collection: Global Coverage Memory and Neuro-Physical Action Control with FSCM-MAPPO

The increasing deployment of Unmanned Aerial Vehicle (UAV) swarms in Internet of Things (IoT) systems enables large-scale cooperative data collection. However, cooperative data collection by UAV swarms remains difficult due to sparse rewards, redundant exploration, inefficient coordination, and the need to refine discrete policy proposals before environment execution. To address these limitations, this paper studies cooperative UAV target collection in randomized simulated environments with observable target and obstacle locations and proposes a Full Spatial Coverage Memory Multi-Agent Proximal Policy Optimization (FSCM-MAPPO), a cooperative data collection algorithm integrating global perception grid with dynamic shared memory, a dense reward shaping mechanism, and a neuro-physical hybrid action controller. Specifically, a global perception grid with dynamic shared memory provides explicit spatial memory and coverage awareness, in which the global coverage information is complementary to each UAV’s local observation, thereby alleviating reward sparsity and redundant search. A neuro-physical hybrid action controller refines policy outputs through local geometric screening, velocity-based intent refinement, and discrete action selection. In an enhanced Multi-Agent Particle Environment with dynamic energy constraints, FSCM-MAPPO attains 71.80±9.73% full-task success and 95.77±1.67% target collection across five independent seeds under deterministic action selection, outperforming the tested MARL baselines. Ablations further show that coverage memory improves visitation and reduces repeated visits, while dense shaping and hybrid control aid task completion. Furthermore, fixed-policy comparisons reveal that hybrid control reduces accumulated contact exposure but increases contact-entry events, indicating a trade-off between task completion and contact behavior.