Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omni-directional target with multiple, homogeneous agents that are subject to unicycle kinematic constraints. We use shared experience to train a policy for a given number of pursuers that is executed independently by each agent at run-time. The training benefits from curriculum learning, a sweeping-angle ordering to locally represent neighboring agents and encouraging good formations with reward structure that combines individual and group rewards. Simulated experiments with a reactive evader and up to eight pursuers show that our learning-based approach, with non-holonomic agents, performs on par with classical algorithms with omni-directional agents, and outperforms their non-holonomic adaptations. The learned policy is successfully transferred to the real world in a proofof-concept demonstration with three motion-constrained pursuer drones.
A behavioral-based strategy for cooperative hunting using drones is proposed in this paper. In this decentralized scheme, each drone acts as an individual agent computing its guidance strategy toward the target based on the relative position of its neighbors without the use of direct communication. The algorithm is based on the deviated pure pursuit methodology, and the emerged behavior mimics a natural hunting formation. Simulations and real-time experiments with varying conditions were carried out to validate the effectiveness of the proposed hunting scheme. Videos of the system in action can be seen on: https://youtu.be/g2dODbd6ZLA.
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