Fault-Tolerant Control for Multi-UAV Exploration System via Reinforcement Learning Algorithm

In the UAV swarm, the degradation in the health status of some UAVs often brings negative effects to the system. To compensate for the negative effect, we present a fault-tolerant Multi-Agent Reinforcement Learning Algorithm that can control an unstable Multiple Unmanned Aerial Vehicle (Multi-UAV) system to perform exploration tasks. Different from traditional multi-agent methods that require the agents to remain healthy during task execution, our approach breaks this limitation and allows the agents to change status during the task. In our algorithm, the agent can accept both the adjacency state matrix about the neighboring agents and a kind of healthy status vector to integrate both and generate the communication topology. During this process, the agents with poor health status are given more attention for returning to normal status. In addition, we integrate a temporal convolution module into our algorithm and enable the agent to capture the temporal information during the task. We introduce a scenario regarding Multi-UAV ground exploration, where the health status of UAVs gradually weakens over time before dropping into a fault status; the UAVs require rescues from time to time. We conduct some experiments in this scenario and verify our algorithm. Our algorithm can increase the drone's survival rate and make the swarm perform better.