A Fast Path-Planning Method for Continuous Harvesting of Table-Top Grown Strawberries

2025-01-09

AI Summary

Traditional path-planning algorithms (e.g., RRT, A*) suffer from poor real-time performance, excessive path redundancy, and suboptimality in continuous desktop-scale strawberry harvesting. Method: This paper proposes an Interactive Local Minimum Search Algorithm (ILMSA), the first to introduce an iterative expansion-and-refinement strategy centered on local minima. It leverages multi-plane 2D environmental projections for efficient 3D path generation and integrates path-segment composition with B-spline smoothing to ensure motion continuity. Contribution/Results: Experiments demonstrate that ILMSA reduces path length by 21.5% and accelerates planning by 97.1% over 3D-RRT; in 2D scenarios, it achieves 16.2–23.4% shorter paths and >96% faster computation than A*-based methods. Field tests show total execution time and path length are only 58% and 69% of those achieved by the LPS algorithm, respectively—significantly enhancing efficiency and practicality for robotic strawberry harvesting.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Continuous harvesting and storage of multiple fruits in a single operation allow robots to significantly reduce the travel distance required for repetitive back-and-forth movements. Traditional collision-free path planning algorithms, such as Rapidly-Exploring Random Tree (RRT) and A-star (A), often fail to meet the demands of efficient continuous fruit harvesting due to their low search efficiency and the generation of excessive redundant points. This paper presents the Interactive Local Minima Search Algorithm (ILMSA), a fast path-planning method designed for the continuous harvesting of table-top grown strawberries. The algorithm featured an interactive node expansion strategy that iteratively extended and refined collision-free path segments based on local minima points. To enable the algorithm to function in 3D, the 3D environment was projected onto multiple 2D planes, generating optimal paths on each plane. The best path was then selected, followed by integrating and smoothing the 3D path segments. Simulations demonstrated that ILMSA outperformed existing methods, reducing path length by 21.5% and planning time by 97.1% compared to 3D-RRT, while achieving 11.6% shorter paths and 25.4% fewer nodes than the Lowest Point of the Strawberry (LPS) algorithm in 3D environments. In 2D, ILMSA achieved path lengths 16.2% shorter than A, 23.4% shorter than RRT, and 20.9% shorter than RRT-Connect, while being over 96% faster and generating significantly fewer nodes. Field tests confirmed ILMSA's suitability for complex agricultural tasks, having a combined planning and execution time and an average path length that were approximately 58% and 69%, respectively, of those achieved by the LPS algorithm.

Problem

• Path Planning
• Efficient Algorithm
• Strawberry Harvesting

Innovation

• ILMSA
• 3D to 2D path planning
• agricultural harvesting efficiency