Learning Tethered Perching for Aerial Robots
Fabian Hauf, Başaran Bahadır Koçer, Alan Slatter, Hai-Nguyen Nguyen, Oscar Pang, Ronald Clark, Edward Johns, Mirko Kovač
Abstract
Aerial robots have a wide range of applications, such as collecting data in hard-to-reach areas. This requires the longest possible operation time. However, because currently available commercial batteries have limited specific energy of roughly 300 W h kg <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , a drone's flight time is a bottleneck for sustainable long-term data collection. Inspired by birds in nature, a possible approach to tackle this challenge is to perch drones on trees, and environmental or man-made structures, to save energy whilst in operation. In this paper, we propose an algorithm to automatically generate trajectories for a drone to perch on a tree branch, using the proposed tethered perching mechanism with a pendulum-like structure. This enables a drone to perform an energy-optimised, controlled 180° flip to safely disarm upside down. To fine-tune a set of reachable trajectories, a soft actor critic-based reinforcement algorithm is used. Our experimental results show the feasibility of the set of trajectories with successful perching. Our findings demonstrate that the proposed approach enables energy-efficient landing for long-term data collection tasks.