Chasing the Intruder: A Reinforcement Learning Approach for Tracking Intruder Drones

TL;DR

This paper presents a reinforcement learning-based system for autonomous detection and tracking of intruder drones using a chaser drone, integrating computer vision and policy learning in simulation.

Contribution

It introduces a novel reinforcement learning framework combined with computer vision for drone intruder tracking, implemented in ROS and Gazebo.

Findings

Reinforcement learning policy successfully converges for drone tracking.

The learned policy is robust to changes in intruder drone speed and orientation.

System demonstrates effective tracking in simulated environments.

Abstract

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.