提出快速路径规划算法，提升草莓采摘机器人作业效率。
A Fast Path-Planning Method for Continuous Harvesting of Table-Top Grown Strawberries
- 基于局部极小点交互式扩展，动态优化无碰撞路径段。
- 3D环境下路径缩短21.5%，规划时间减少97.1%。
- 适合复杂农田场景，实测效率比现有方法高近60%。
连续采摘多颗桌栽草莓并一次性存储，可显著减少机器人重复往返的移动距离。传统无碰撞路径规划算法如RRT和A*因搜索效率低、冗余点过多，难以满足高效连续采摘需求。本文提出交互式局部极小点搜索算法（ILMSA），通过迭代扩展与优化局部极小点生成的无碰撞路径段，实现快速路径规划。为支持三维环境，将3D空间投影至多个2D平面，分别生成最优路径后融合平滑为3D路径。仿真表明，相比3D-RRT，ILMSA路径长度减少21.5%，规划时间降低97.1%；相较于LPS算法，在3D环境中路径缩短11.6%，节点数减少25.4%。在2D环境下，路径比A短16.2%，比RRT短23.4%，比RRT-Connect短20.9%，速度超96%，节点数显著更少。田间测试验证其适用于复杂农业任务，整体规划与执行时间约为LPS算法的58%，平均路径长度为69%。
原文摘要 · Abstract (English)
Continuous harvesting and storage of multiple fruits in a single operation allow robots to significantly reduce the travel distance required for repetitive back-and-forth movements. Traditional collision-free path planning algorithms, such as Rapidly-Exploring Random Tree (RRT) and A-star (A), often fail to meet the demands of efficient continuous fruit harvesting due to their low search efficiency and the generation of excessive redundant points. This paper presents the Interactive Local Minima Search Algorithm (ILMSA), a fast path-planning method designed for the continuous harvesting of table-top grown strawberries. The algorithm featured an interactive node expansion strategy that iteratively extended and refined collision-free path segments based on local minima points. To enable the algorithm to function in 3D, the 3D environment was projected onto multiple 2D planes, generating optimal paths on each plane. The best path was then selected, followed by integrating and smoothing the 3D path segments. Simulations demonstrated that ILMSA outperformed existing methods, reducing path length by 21.5% and planning time by 97.1% compared to 3D-RRT, while achieving 11.6% shorter paths and 25.4% fewer nodes than the Lowest Point of the Strawberry (LPS) algorithm in 3D environments. In 2D, ILMSA achieved path lengths 16.2% shorter than A, 23.4% shorter than RRT, and 20.9% shorter than RRT-Connect, while being over 96% faster and generating significantly fewer nodes. Field tests confirmed ILMSA's suitability for complex agricultural tasks, having a combined planning and execution time and an average path length that were approximately 58% and 69%, respectively, of those achieved by the LPS algorithm.
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