Navigation of Apple Tree Pruning Robot Based on Improved RRT-Connect Algorithm

Yechen Li and Shaochun Ma ()
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Yechen Li: Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
Shaochun Ma: Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2023, vol. 13, issue 8, 1-20

Abstract: Pruning branches of apple trees is a labor-intensive task. Pruning robots can save manpower and reduce costs. A full map of the apple orchard with collision-free paths, which is navigation planning, is essential. To improve the navigation efficiency of the apple tree pruning robot, an improved RRT-Connect algorithm was proposed. Firstly, to address the disadvantage of randomness in the expansion of the RRT-Connect algorithm, a goal-biased strategy was introduced. Secondly, to shorten the path length, the mechanism of the nearest node selection was optimized. Finally, the path was optimized where path redundancy nodes were removed, and Bezier curves were used to deal with path sharp nodes to further reduce the path length and improve the path smoothness. The experimental results of apple orchard navigation show that the improved algorithm proposed in this paper can cover the whole apple orchard, and the path length is 32% shorter than that of the RRT-Connect algorithm. The overall navigation time is 35% shorter than that of the RRT-Connect algorithm. This shows that the improved algorithm has better adaptability and planning efficiency in the apple orchard environment. This will contribute to the automation of orchard operations and provide valuable references for future research on orchard path planning.

Keywords: apple orchard; goal-biased strategy; navigation; pruning robot; RRT-Connect (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: 2023
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