Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning
This addresses robust real-time control for drone-based additive manufacturing in dynamic environments, though it is incremental as it adapts existing DRL methods to a specific domain.
The paper tackled motion control for multi-rotor drones in additive manufacturing by applying deep reinforcement learning, resulting in TD3 achieving consistent success and stability under mass variability.
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.