A large group of small, limited endurance autonomous vehicles working cooperatively may be more effective in target search and track operations when compared with a long endurance vehicle. For a persistent search and track task, a need exists for coordination algorithms that account for limited agent endurance. This paper presents a multi-agent persistent search and track algorithm incorporating endurance constraints in a high-level algorithm that deploys and recovers vehicles from a stationary base station. Agents are assigned to search, track, return, and deploy modes using on-board sensor and battery measurements. Simulations and experiments show the relationship between the number of agents, battery capacity, search performance, and target tracking performance. The measures used to quantify these relationships include spatiotemporal coverage, target tracking effectiveness, and the usage of available aircraft. Hardware experiments demonstrate the effectiveness of the approach.
Multi-User Position Based on Trajectories-Aware Handover Strategy for Base Station Selection with Multi-Agent Learning
