用深度强化学习让四轮转向农机在田间自主导航，实现复杂路径跟踪。
Autonomous Navigation of 4WIS4WID Agricultural Field Mobile Robot using Deep Reinforcement Learning
- 将四轮转向参数化为两个变量，支持横向移动与零半径转弯
- 在模拟田间环境中实现多作物行点对点自主导航，路径追踪误差小
- 对比多种连续动作强化学习算法，验证方法可行性，适合智能农业场景
在适配农业4.0的未来农田中，机器人需在作物间自主导航以完成喷药、采摘等任务，但受限于非几何障碍物、空间约束和户外环境，导航极具挑战。本文采用深度强化学习（DRL）解决四轮独立转向四轮驱动（4WIS4WID）农机在结构化自动化农田中的导航问题。研究包含三部分：四轮转向配置的参数化、基于DRL的作物行跟踪，以及多作物行环境下的自主导航。通过将多种转向模式（如对称四轮转向、零半径转向及横向移动模式）统一为两个参数表示，实现了灵活运动控制。利用DRL成功跟踪不规则作物行路径，且在含航点的多行仿真环境中实现高效点对点导航。进一步对比了多种适用于连续动作的DRL算法，验证了方法的有效性。
原文摘要 · Abstract (English)
In the futuristic agricultural fields compatible with Agriculture 4.0, robots are envisaged to navigate through crops to perform functions like pesticide spraying and fruit harvesting, which are complex tasks due to factors such as non-geometric internal obstacles, space constraints, and outdoor conditions. In this paper, we attempt to employ Deep Reinforcement Learning (DRL) to solve the problem of 4WIS4WID mobile robot navigation in a structured, automated agricultural field. This paper consists of three sections: parameterization of four-wheel steering configurations, crop row tracking using DRL, and autonomous navigation of 4WIS4WID mobile robot using DRL through multiple crop rows. We show how to parametrize various configurations of four-wheel steering to two variables. This includes symmetric four-wheel steering, zero-turn, and an additional steering configuration that allows the 4WIS4WID mobile robot to move laterally. Using DRL, we also followed an irregularly shaped crop row with symmetric four-wheel steering. In the multiple crop row simulation environment, with the help of waypoints, we effectively performed point-to-point navigation. Finally, a comparative analysis of various DRL algorithms that use continuous actions was carried out.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。