Harvest sequence planning algorithm for grapes based on interference fruit cluster removal and distance adaptive clustering

Abstract

To reduce potential collisions and damage that may occur when harvesting multiple adjacent targets with a robotic harvester, this study proposes a grape harvesting sequence planning algorithm based on the removal of interference grape clusters and sampling distance adaptive clustering. First, the locations of grape clusters in distant scenes were identified using YOLOv8n. Subsequently, a method for removing background grape clusters and a distance adaptive clustering method for grapes were introduced, enabling adaptive harvesting cluster partitioning at varying sampling distances. Finally, a harvesting order planning algorithm for inter-cluster picking was proposed by integrating expert experience with the operational direction of the harvesting robot. Experimental results indicate that the distance adaptive clustering method demonstrated stronger adaptability for various sampling distances than the original DBSCAN clustering algorithm, and it is capable of grouping nearby grapes into a single picking cluster. The intra-cluster harvesting sequence planning algorithm effectively facilitated decision-making regarding the priority of grape picking cluster harvesting, aligning the harvesting sequence planning results with manually planned outcomes and providing essential information to guide the robot for low-damage harvesting.

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