Safe Decentralized Multi-UAV Dynamic Target Search Under Energy Constraints via Multi-Agent Reinforcement Learning

Abstract

Dynamic target search using multiple unmanned aerial vehicles (UAVs) is challenging due to stochastic target emergence, partial observability, and strict onboard energy limitations. This paper proposes an energy-aware decentralized search framework for multi-UAV dynamic target search in uncertain environments. A dual-information hexagonal map is introduced to simultaneously represent environmental uncertainty and target existence probability, enabling informationdriven search decisions under partial observability. The problem is formulated as a constrained multi-agent partially observable Markov decision process (CMAPOMDP), and a safe multiagent reinforcement learning (MARL) approach, namely multiagent proximal policy optimization (MAPPO)-Lagrangian, is developed to learn decentralized policies while enforcing UAV energy constraints. Simulation results demonstrate that the proposed framework achieves higher search efficiency and strictly satisfies energy safety requirements compared with several baseline methods in dynamic target search scenarios.