The autonomous control of flippers plays an important role in enhancing the intelligent operation of tracked robots within complex environments. While existing methods mainly rely on hand-crafted control models, in this paper, we introduce a novel approach that leverages deep reinforcement learning (DRL) techniques for autonomous flipper control in complex terrains. Specifically, we propose a new DRL network named AT-D3QN, which ensures safe and smooth flipper control for tracked robots. It comprises two modules, a feature extraction and fusion module for extracting and integrating robot and environment state features, and a deep Q-Learning control generation module for incorporating expert knowledge to obtain a smooth and efficient control strategy. To train the network, a novel reward function is proposed, considering both learning efficiency and passing smoothness. A simulation environment is constructed using the Pymunk physics engine for training. We then directly apply the trained model to a more realistic Gazebo simulation for quantitative analysis. The consistently high performance of the proposed approach validates its superiority over manual teleoperation.

翻译：摆臂的自主控制对提升履带机器人在复杂环境中的智能作业能力具有重要作用。现有方法主要依赖手工设计的控制模型，而本文提出了一种创新方法，利用深度强化学习技术实现复杂地形下摆臂的自主控制。具体而言，我们设计了一种名为AT-D3QN的新型深度强化学习网络，能够确保履带机器人摆臂控制的安全性与平稳性。该网络包含两个模块：特征提取融合模块用于提取并融合机器人与环境的状态特征，深度Q学习控制生成模块则通过引入专家知识获取平滑高效的控制策略。为训练该网络，我们提出了一种兼顾学习效率与通行平稳性的新型奖励函数，并基于Pymunk物理引擎构建了仿真训练环境。随后，我们将训练完成的模型直接应用于更真实的Gazebo仿真环境进行定量分析。该方法持续优异的表现验证了其相较于人工遥控操作的显著优越性。