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

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 article 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 3-D, the 3-D environment was projected onto multiple 2-D planes, generating optimal paths on each plane. The best path was then selected, followed by integrating and smoothing the 3-D path segments. Simulations demonstrated that ILMSA outperformed existing methods, reducing path length by 21.5% and planning time by 97.1% compared to 3-D rapidly-exploring random tree, while achieving 11.6% shorter paths and 25.4% fewer nodes than the lowest point of the strawberry (LPS) algorithm in 3-D environments. In 2-D, 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. In addition, ILMSA outperformed the partially guided Q-learning method, reducing path length by 36.7%, shortening planning time by 97.8%, and effectively avoiding entrapment in complex scenarios. 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.