A Vision-Aided Approach to Perching a Bioinspired Unmanned Aerial Vehicle

This paper presents the implementation of a machine learning approach for replicating highly adaptive avian perching behavior. With full consideration of both the configuration of flying vehicles and perching principles, a bioinspired aerial robot comprising one flight subsystem and one perching subsystem is designed. Based on the real-time landing speed and attitude, a novel type of soft grasping mechanism for dexterous perching is proposed to provide adhesive force and absorb impact force. During the critical perching phase, the dynamics of the perching actuator change with the touchdown conditions and the type of perching target. A hybrid automation of a time-to-contact theory-based attitude controller and a robust self-localization system are utilized to regulate the desired perching maneuvers. The experimental results are provided to attest to the effectiveness of the proposed perching method.