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

2025-06-16

AI Summary

In construction hoisting, underactuated suspended aerial manipulator platforms struggle to reach elevated targets due to thrust limitations. Method: This paper proposes a hierarchical reinforcement learning control framework built upon model predictive control (MPC). A proximal policy optimization (PPO) agent operates in the null space of high-level tasks to dynamically modulate low-level reference trajectories, thereby decoupling and coordinating swing-up motion with high-priority end-effector position/orientation control. The method integrates task-priority mapping, null-space projection, and closed-loop simulation training. Results: In high-fidelity numerical simulations, the system robustly achieves swing-up and precise定点 stabilization across multiple initial conditions, achieving end-effector position errors < 2 cm and orientation errors < 1°. It significantly outperforms purely model-based approaches in robustness. This framework establishes a verifiable, end-to-end learning-based control paradigm for underactuated aerial manipulation platforms.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Control swing-up maneuvers for suspended aerial platforms
• Enable perching at inaccessible locations using RL
• Maintain high-priority tasks during maneuver execution

Innovation

• Hierarchical control framework for task prioritization
• Reinforcement learning adjusts lower-priority task set-points
• Swing-up maneuver in nullspace of high-priority tasks