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- W4381551733 abstract "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." @default.
- W4381551733 created "2023-06-22" @default.
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- W4381551733 date "2023-06-17" @default.
- W4381551733 modified "2023-09-27" @default.
- W4381551733 title "Deep Reinforcement Learning for Flipper Control of Tracked Robots" @default.
- W4381551733 doi "https://doi.org/10.48550/arxiv.2306.10352" @default.
- W4381551733 hasPublicationYear "2023" @default.
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