用最优路径+实时控制，让农机在田里不压草、少绕路。
Multi-Waypoint Path Planning and Motion Control for Non-holonomic Mobile Robots in Agricultural Applications
- 先解带曲率约束的最短路径问题，再用模型预测控制精准走点
- 比传统方法路径短16%，且更平滑，减少土壤损伤
- 适合需要精准避障和省时省力的农业机器人应用
自动驾驶移动机器人在非结构化农田环境中的导航需求日益增长。例如草地杂草控制任务需高效遍历一组无序坐标点，同时最小化行驶距离并满足曲率约束以避免土壤破坏和植被损伤。本文提出一种集成导航框架，结合基于杜宾旅行商问题（DTSP）的全局路径规划与非线性模型预测控制（NMPC）的局部路径规划及控制策略。DTSP生成最短、曲率受限的路径以高效访问所有目标点，而NMPC则利用该路径计算控制信号，确保机器人准确抵达每个航点。在真实田间数据集上的对比仿真验证表明，基于DTSP的耦合规划方法相比解耦方法路径缩短约16%，且更平滑；在此基础上，NMPC控制器有效引导机器人到达指定航点，同时局部优化轨迹并满足约束条件。结果表明该框架在农业环境中具有高效自主导航的潜力。
原文摘要 · Abstract (English)
There is a growing demand for autonomous mobile robots capable of navigating unstructured agricultural environments. Tasks such as weed control in meadows require efficient path planning through an unordered set of coordinates while minimizing travel distance and adhering to curvature constraints to prevent soil damage and protect vegetation. This paper presents an integrated navigation framework combining a global path planner based on the Dubins Traveling Salesman Problem (DTSP) with a Nonlinear Model Predictive Control (NMPC) strategy for local path planning and control. The DTSP generates a minimum-length, curvature-constrained path that efficiently visits all targets, while the NMPC leverages this path to compute control signals to accurately reach each waypoint. The system's performance was validated through comparative simulation analysis on real-world field datasets, demonstrating that the coupled DTSP-based planner produced smoother and shorter paths, with a reduction of about 16% in the provided scenario, compared to decoupled methods. Based thereon, the NMPC controller effectively steered the robot to the desired waypoints, while locally optimizing the trajectory and ensuring adherence to constraints. These findings demonstrate the potential of the proposed framework for efficient autonomous navigation in agricultural environments.
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