Target encirclement for multiple quadrotors with visual detection and localization

Abstract

Target encirclement is widely used in the field of unmanned aerial vehicles(UAVs), which can effectively monitor and intercept external threats. However, the integration from target detection, localization to final tracking is difficult or costly. This article proposes a complete and inexpensive framework of the target encirclement for multiple quadrotors. The framework consists of three modules: object detection, target localization and formation tracking. Firstly, a one-stage object detector based on a convolutional neural network is used to achieve fast and accurate object detection. Then, combined with the position and attitude states of the quadrotor, a 3D target localization scheme to locate the target position is proposed. Based on consensus theory, a time-varying formation tracking control protocol is proposed. Finally, a multiple quadrotor platform composed of one reconnaissance quadrotor and four hunter quadrotors is built with self-organizing network communication, which avoids the expensive cost of deploying object detection modules on each quadrotor platform. We deployed the framework on the multiple quadrotor platform and conducted static and dynamic localization and encirclement experiments with a minibus as the target. The result shows that the reconnaissance quadrotors can detect and accurately locate targets over 30 fps, and the average deviation of locating the target minibus could reach a minimum of 0.0712 m. The hunter quadrotors could track and encircle the dynamic moving target minibus in a time-varying formation. Experiments demonstrate the effectiveness and practicality of the proposed framework of the target encirclement for multiple quadrotors.