用深度强化学习让无人机在增材制造中稳定飞行，适应负载变化。
Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning
- 采用TD3算法结合课程学习，让无人机自主学会飞行控制。
- 在负载变化下仍保持高成功率和控制稳定性，优于传统方法。
- 适合需要自动飞行的工业级无人机场景，如大型或危险环境施工。
本文研究将深度强化学习（DRL）应用于多旋翼无人机在增材制造（AM）中的运动控制问题。基于无人机的增材制造可实现大范围或危险环境下的灵活、自主材料沉积，但面对负载变化与外部扰动时，实现鲁棒的实时控制仍具挑战。传统控制器如PID需频繁调参，难以适应动态场景。本文提出一种DRL框架，通过课程学习策略训练多旋翼无人机在增材制造任务中的航点导航控制策略。对比了深度确定性策略梯度（DDPG）与孪生延迟深度确定性策略梯度（TD3），实验表明：在引入质量变化的情况下，TD3在训练稳定性、控制精度和任务成功率上表现更优。该结果为实现增材制造中可扩展、鲁棒的无人机自主控制提供了可行路径。
原文摘要 · Abstract (English)
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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