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论文arXiv 2611.07293今天

遗传算法优化果蔬采摘序:动态航点策略提升接近成功率至70.7% ​

本文由 AI 根据公开内容整理,可能有误,请以原文为准:An Ordering Optimization for Fruit-Vegetable in High-Density Environment based on Genetic Algorithm(arXiv 2611.07293)

核心内容 ​

本文提出一种面向高密度非结构化作物环境的机器人采摘方法,融合全局RGB-D相机三维定位、末端近距离观测及基于遗传算法(GA)的采摘序列优化。研究在实验室及4至6果田间场景完成评估,通过参数敏感性分析对比了静态与动态航点对运动效率的影响,证实连续执行策略可显著减少无效回零运动,为复杂农业环境下的自动化作业提供量化依据。

系统采用全局RGB-D获取果实坐标供GA排序,末端相机负责执行期近距感知。核心创新在于动态航点机制:在不改变GA既定序列前提下,根据剩余果实分布实时调整中间路径点。田间实测表明,该策略使目标接近成功率达70.7%(静态为65.3%),碰撞次数由55次降至40次,证明了自适应路径规划在降低物理干涉风险方面的工程价值。

关键看点 ​

  • 动态航点机制:不改变GA序列,依据剩余果实分布实时更新中间路径点,消除固定航点导致的冗余运动与潜在碰撞风险
  • 双级视觉感知:全局RGB-D相机提供3D坐标用于排序规划,末端相机在执行阶段进行近距离观测校正,适配非结构化环境
  • 田间实测数据:动态航点连续策略实现70.7%目标接近成功率,较静态策略提升5.4个百分点,碰撞事件减少27%
  • 连续执行优化:摒弃传统单果采摘后返回Home点模式,通过 waypoints 定义中间运动,大幅压缩无效空行程时间

工程与产业观察 ​

该研究揭示了在非结构化农业场景中,单纯依赖全局最优序列不足以解决物理执行问题。动态航点策略将离散的组合优化与连续的几何约束解耦,为具身智能在密集障碍物环境下的任务规划提供了“全局排序+局部自适应”的工程范式,对提升采摘机器人实际作业鲁棒性具有直接参考价值。

英文摘要 (Abstract) ​

This paper presents a robotic harvesting method that combines RGB-D fruit localization, GA-based harvest ordering, and waypoint-based robot motion in dense and unstructured crop environments. The global RGB-D camera provides 3D fruit positions for ordering, while the end-effector-mounted camera provides close-range observation during execution. The GA determines the harvesting sequence, and static or dynamic waypoints define the intermediate robot motion. The method was evaluated in a controlled laboratory testbed and in four-, five-, and six-fruit field conditions. GA solutions were compared with exhaustive enumeration, and both waypoint offsets were examined by parameter sensitivity analysis. Continuous execution reduced unnecessary Home-return motion, while the dynamic waypoint changed with the remaining fruit distribution without changing the GA sequence. In the field evaluation, the dynamic-waypoint continuous strategy produced 40 collision events and a 70.7% target-approach success rate, compared with 55 collision events and 65.3% for the static-waypoint continuous strategy.

作者:Jaehwi Seol, Jeonghyeon Pak, Hyoung Il Son

基于本页技术方向关联的整机参数、核心零部件、实测报告与同赛道选型横评。

来源

本页事实对应的公开页面,以原文为准。

  1. arXiv 2611.07293:An Ordering Optimization for Fruit-Vegetable in High-Density Environment based on Genetic Algorithm访问 2026-11-06