An Ordering Optimization for Fruit-Vegetable in High-Density Environment based on Genetic Algorithm
Overview
This paper presents a robotic harvesting method combining RGB-D fruit localization, genetic algorithm-based harvest ordering, and waypoint-based robot motion. The approach was evaluated in laboratory and field conditions with varying numbers of fruits. Results indicate that a dynamic-waypoint continuous strategy reduced collision events compared to a static-waypoint strategy, though it achieved a slightly higher target-approach success rate.
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Event evidence and corrections
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Environment: controlled real
“vesting 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”
Exact source · revision 1Source owner not reported
Reported quantity · collision events: 40 other · Basis not reported · Differing source assertions
“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.”
Exact source · revision 1Source owner not reported
Reported quantity · collision events: 55 other · Basis not reported · Differing source assertions
“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.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalAn Ordering Optimization for Fruit-Vegetable in High-Density Environment based on Genetic Algorithm
This paper presents a robotic harvesting method combining RGB-D fruit localization, genetic algorithm-based harvest ordering, and waypoint-based robot motion. The approach was evaluated in laboratory and field conditions with varying numbers of fruits. Results indicate that a dynamic-waypoint continuous strategy reduced collision events compared to a static-waypoint strategy, though it achieved a slightly higher target-approach success rate.
Event coverage history
Current attention 3·Peak within the comparable range 10（2026-11-06 06:00 UTC）·Change within the comparable range over 24 hours -49%
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