用姿态信息增强强化学习，提升滑移底盘车辆视觉导航能力
Experimental investigation of pose informed reinforcement learning for skid-steered visual navigation
- 将车辆姿态信息融入强化学习，构建更可靠的视觉导航策略
- 在软硬件实验中表现优于现有方法，显著降低偏离路径率
- 适合研究自动驾驶车辆控制与复杂地形自主导航的学者
基于视觉的车道保持在机器人和自动驾驶地面车辆领域具有重要意义，广泛应用于公路及非公路场景。滑移转向车辆平台虽适用于人工操作，但其轮地滑移交互（尤其在非公路环境下）缺乏系统建模，成为自动化部署的瓶颈。端到端学习方法如模仿学习和深度强化学习因无需精确解析模型而受到关注。然而，在动态工况下（特别是滑移转向车辆）的系统化设计与验证仍处于探索阶段。本文提出一种结构化学习视觉导航的新方法，并通过大量软件仿真、硬件测试及消融实验验证其有效性。结果表明，该方法在性能上显著优于现有文献水平。
原文摘要 · Abstract (English)
Vision-based lane keeping is a topic of significant interest in the robotics and autonomous ground vehicles communities in various on-road and off-road applications. The skid-steered vehicle architecture has served as a useful vehicle platform for human controlled operations. However, systematic modeling, especially of the skid-slip wheel terrain interactions (primarily in off-road settings) has created bottlenecks for automation deployment. End-to-end learning based methods such as imitation learning and deep reinforcement learning, have gained prominence as a viable deployment option to counter the lack of accurate analytical models. However, the systematic formulation and subsequent verification/validation in dynamic operation regimes (particularly for skid-steered vehicles) remains a work in progress. To this end, a novel approach for structured formulation for learning visual navigation is proposed and investigated in this work. Extensive software simulations, hardware evaluations and ablation studies now highlight the significantly improved performance of the proposed approach against contemporary literature.
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