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

TL;DR

This work tackles the need for fast, collision-free path planning to enable continuous harvesting of table-top strawberries. It introduces the Interactive Local Minima Search Algorithm (ILMSA), which combines iterative 2D node expansion with 3D spatial reasoning via plane projections and B-spline smoothing to produce smooth, collision-free trajectories. Across simulations in simple and complex environments and in field tests, ILMSA consistently outperforms conventional planners (3D-RRT, LPS, A*, RRT-Connect) and learning-based methods (QAPF) in path length, node count, and planning speed, while maintaining safety around delicate stems. The integration within a ROS-based continuous harvesting system demonstrates practical impact for real-time agricultural robotics, with implications for more efficient, scalable automated harvesting.

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.

Figures (20)

• Figure 1: Table-top grown strawberries picking scenario.
• Figure 2: Environment for planning the picking path of table-top grown strawberries: (a) 3D environment, (b) 2D simplified environment.
• Figure 3: Continuous harvesting control system.
• Figure 4: Visual perception scenario.
• Figure 5: Schematic diagram of the process of expanding path nodes.