Multi-Strategy Fusion RRT-Based Algorithm for Optimizing Path Planning in Continuous Cherry Picking

Yi Zhang, Xinying Miao (), Yifei Sun, Zhipeng He, Tianwen Hou, Zhenghan Wang and Qiuyan Wang
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Yi Zhang: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Xinying Miao: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Yifei Sun: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Zhipeng He: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Tianwen Hou: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Zhenghan Wang: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Qiuyan Wang: Dalian Modern Agricultural Production Development Service Center, Dalian 116036, China

Agriculture, 2025, vol. 15, issue 15, 1-19

Abstract: Automated cherry harvesting presents a significant opportunity to overcome the high costs and inefficiencies of manual labor in modern agriculture. However, robotic harvesting in dense canopies requires sophisticated path planning to navigate cluttered branches and selectively pick target fruits. This paper introduces a complete robotic harvesting solution centered on a novel path-planning algorithm: the Multi-Strategy Integrated RRT for Continuous Harvesting Path (MSI-RRTCHP) algorithm. Our system first employs a machine vision system to identify and locate mature cherries, distinguishing them from unripe fruits, leaves, and branches, which are treated as obstacles. Based on this visual data, the MSI-RRTCHP algorithm generates an optimal picking trajectory. Its core innovation is a synergistic strategy that enables intelligent navigation by combining probability-guided exploration, goal-oriented sampling, and adaptive step size adjustments based on the obstacle’s density. To optimize the picking sequence for multiple targets, we introduce an enhanced traversal algorithm ( σ -TSP) that accounts for obstacle interference. Field experiments demonstrate that our integrated system achieved a 90% picking success rate. Compared with established algorithms, the MSI-RRTCHP algorithm reduced the path length by up to 25.47% and the planning time by up to 39.06%. This work provides a practical and efficient framework for robotic cherry harvesting, showcasing a significant step toward intelligent agricultural automation.

Keywords: RRT; multi-strategy fusion; path planning; multi-objective traversal (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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