Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach for low-level pose control of tilting multirotors, enabling wall-perching tasks with improved robustness and efficiency, trained initially in simulation and refined with real-world data.

Contribution

It is the first to apply reinforcement learning to tilting multirotors, proposing a novel reward function and state representation to enhance control performance.

Findings

Successfully controlled tilting multirotors in real-world experiments.

Demonstrated robustness against complex dynamics of tilting multirotors.

Achieved efficient power usage through a specialized reward function.

Abstract

Recently, needs for unmanned aerial vehicles (UAVs) that are attachable to the wall have been highlighted. As one of the ways to address the need, researches on various tilting multirotors that can increase maneuverability has been employed. Unfortunately, existing studies on the tilting multirotors require considerable amounts of prior information on the complex dynamic model. Meanwhile, reinforcement learning on quadrotors has been studied to mitigate this issue. Yet, these are only been applied to standard quadrotors, whose systems are less complex than those of tilting multirotors. In this paper, a novel reinforcement learning-based method is proposed to control a tilting multirotor on real-world applications, which is the first attempt to apply reinforcement learning to a tilting multirotor. To do so, we propose a novel reward function for a neural network model that takes power efficiency into account. The model is initially trained over a simulated environment and then fine-tuned using real-world data in order to overcome the sim-to-real gap issue. Furthermore, a novel, efficient state representation with respect to the goal frame that helps the network learn optimal policy better is proposed. As verified on real-world experiments, our proposed method shows robust controllability by overcoming the complex dynamics of tilting multirotors.