Learning Swing-up Maneuvers for a Suspended Aerial Manipulation Platform in a Hierarchical Control Framework

TL;DR

This paper introduces a hierarchical control framework augmented with reinforcement learning to enable swing-up maneuvers for suspended aerial platforms, enhancing their ability to perch at inaccessible locations.

Contribution

It presents a novel integration of RL with model-based control in a hierarchical structure for aerial manipulation, specifically for swing-up tasks.

Findings

Successful simulation of swing-up maneuvers

Effective task prioritization in control framework

Enhanced maneuverability of suspended aerial platforms

Abstract

In this work, we present a novel approach to augment a model-based control method with a reinforcement learning (RL) agent and demonstrate a swing-up maneuver with a suspended aerial manipulation platform. These platforms are targeted towards a wide range of applications on construction sites involving cranes, with swing-up maneuvers allowing it to perch at a given location, inaccessible with purely the thrust force of the platform. Our proposed approach is based on a hierarchical control framework, which allows different tasks to be executed according to their assigned priorities. An RL agent is then subsequently utilized to adjust the reference set-point of the lower-priority tasks to perform the swing-up maneuver, which is confined in the nullspace of the higher-priority tasks, such as maintaining a specific orientation and position of the end-effector. Our approach is validated using extensive numerical simulation studies.