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arXiv · 2502.05996

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning

Gaurav Shetty·Mahya Ramezani·Hamed Habibi·Holger Voos·Jose Luis Sanchez-Lopez
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Abstract

This paper investigates the application of Deep Reinforcement (DRL) Learning to address motion control challenges in drones for additive manufacturing (AM). Drone-based additive manufacturing promises flexible and autonomous material deposition in large-scale or hazardous environments. However, achieving robust real-time control of a multi-rotor aerial robot under varying payloads and potential disturbances remains challenging. Traditional controllers like PID often require frequent parameter re-tuning, limiting their applicability in dynamic scenarios. We propose a DRL framework that learns adaptable control policies for multi-rotor drones performing waypoint navigation in AM tasks. We compare Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) within a curriculum learning scheme designed to handle increasing complexity. Our experiments show TD3 consistently balances training stability, accuracy, and success, particularly when mass variability is introduced. These findings provide a scalable path toward robust, autonomous drone control in additive manufacturing.

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Gaurav Shetty, Mahya Ramezani, Hamed Habibi, Holger Voos, Jose Luis Sanchez-Lopez. 2025-02-09. Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning. https://arxiv.org/abs/2502.05996

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