Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2025
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| author | Shetty, Gaurav Ramezani, Mahya Habibi, Hamed Voos, Holger Sanchez-Lopez, Jose Luis |
| author_facet | Shetty, Gaurav Ramezani, Mahya Habibi, Hamed Voos, Holger Sanchez-Lopez, Jose Luis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning Shetty, Gaurav Ramezani, Mahya Habibi, Hamed Voos, Holger Sanchez-Lopez, Jose Luis Robotics Artificial Intelligence 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. |
| title | Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2502.05996 |