侵入ドローンを追跡せよ：強化学習による追跡アプローチ
Chasing the Intruder: A Reinforcement Learning Approach for Tracking Intruder Drones
強化学習とコンピュータビジョンを組み合わせ、追跡ドローンが侵入ドローンを識別・追跡する制御方策を学習する手法を提案した。
著者: Shivam Kainth, Subham Sahoo, Rajtilak Pal, Shashi Shekhar Jha
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
Drones are becoming versatile in a myriad of applications. This has led to the use of drones for spying and intruding into the restricted or private air spaces. Such foul use of drone technology is dangerous for the safety and security of many critical infrastructures. In addition, due to the varied low-cost design and agility of the drones, it is a challenging task to identify and track them using the conventional radar systems. In this paper, we propose a reinforcement learning based approach for identifying and tracking any intruder drone using a chaser drone. Our proposed solution uses computer vision techniques interleaved with the policy learning framework of reinforcement learning to learn a control policy for chasing the intruder drone. The whole system has been implemented using ROS and Gazebo along with the Ardupilot based flight controller. The results show that the reinforcement learning based policy converges to identify and track the intruder drone. Further, the learnt policy is robust with respect to the change in speed or orientation of the intruder drone.