A multi-objective scheme for collision avoidance, swarm cohesion, and target tracking for smart UAVs

This research introduces a Reinforcement Learning (RL) based testbed designed for real-time coordination among multiple Unmanned Aerial Vehicles (UAVs) using actor-critic models to control a randomly manoeuvring target UAV, and a swarm of tracking UAVs. The system simulates realistic multi-agent interactions within a scalable and distributed architecture built on FlightGear and JSBSim. A key feature is the ability of multiple agents to learn independently yet collaboratively, enabling synchronized behaviors and adaptive tracking in dynamic environments. Collision avoidance, swarm cohesion, and target tracking efficiency are ensured through a 3D ellipsoidal spatial model and a Gaussian reward function, which guide agents to maintain optimal relative positions. The integration of an Intrinsic Curiosity Module (ICM) enhances exploration in sparse-reward settings. Experimental results show that the multi-agent swarm UAVs effectively tracks complex and evasive target paths, demonstrating robustness, scalability, and real-time coordination capabilities essential for advanced UAV operations.