To Harvest or Not to Harvest: Mapping and Decision-Making for a Selective Table Grape Harvesting Robot

Abstract

This letter focuses on robotic harvesting of delicate crops such as table grapes, featuring selective harvesting based on individual product properties. The robot detects grape bunches and estimates their positions and quality attributes. However, sensor limitations and occlusions affect data completeness and accuracy, reducing the cost-effectiveness of automated harvesting systems. Determining in real-time the optimal harvesting order in the presence of uncertainty is therefore important for enhancing efficiency and grape quality for growers and consumers. This task is challenging not only due to data uncertainty, but also due to the need to consider factors such as obstructive low-quality bunches. Existing literature often resorts to sub-optimal approaches such as selecting the first available crop. In contrast, we propose (i) a mapping and tracking method based on multiple viewpoints to enhance bunch information quality and (ii) a decision-making algorithm in a decision-tree with a recursive structure based on a constructed reachability graph derived from the map to optimize harvested quality and execution time sequentially.