ISAC-Enabled Multi-UAV Cooperative Sensing Systems for Diverse Tasks: Maximizing Task Completion Rate at Minimal Cost

In this paper, we study a cooperative sensing network with integrated sensing and communication (ISAC) unmanned aerial vehicles (UAVs) for handling diverse sensing tasks with different types, accuracy requirements, and sensing intervals. Unlike existing works that overlook task differences in real-world applications, we aim to enable UAVs to adjust their service time slots for each task and the number of UAVs assigned to the task, preventing excessive resources to tasks with low demand. To this end, we maximize the task completion rate while minimizing cost by optimizing UAV trajectories, task associations, and power control. Given the problem’s mixed-integer nature and the partial observability of UAVs, we design a multi-agent proximal policy optimization with action branching and masking (MAPPO-ABAM) algorithm to tackle it. Simulation results show that our algorithm achieves the highest completion rate and the lowest sensing cost compared to four benchmark methods.