遺伝的アルゴリズムによる高密度環境での果菜収穫順序最適化
An Ordering Optimization for Fruit-Vegetable in High-Density Environment based on Genetic Algorithm
RGB-Dカメラで果実位置を取得し、遺伝的アルゴリズムで収穫順序を最適化、ウェイポイントでロボット動作を制御する高密度作物環境向け収穫手法を提案し、実環境で評価した。
著者: Jaehwi Seol, Jeonghyeon Pak, Hyoung Il Son
分類: cs.RO, eess.SY
原文アブストラクト
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.