Adaptive Multi-UAV Coordination for Heterogeneous Target Search and Connect Missions Using Proximal Policy Optimization

A novel multi-agent reinforcement learning (MARL) technique utilizing Proximal Policy Optimization (PPO) is introduced to coordinate a drone team in search and rescue operations, where multiple targets with different connectivity requirements are present. The proposed approach integrates a dynamic reward system that effectively manages the trade-off between exploration, target identification, and network connectivity. The model is trained for a fixed target type and area size, but is evaluated for scenarios with different numbers of UAVs, diverse target configurations, and mission priorities, including target search and information updates, illustrating its adaptability and versatility in comparison to common methods.