Far-near combined positioning of picking-point based on depth data features for horizontal-trellis cultivated grape

For robotic harvesting of table grape in clusters usually by gripping and cutting the peduncle, the accurate cutting point positioning on the peduncle is crucial. In this paper, a new method for accurate positioning of picking-point based on the combination of far-view and near-view depth data features for horizontal-trellis cultivated grape was proposed. First, a far-near combination strategy for picking-point positioning which makes full use of the features of grape cluster and horizontal-trellis environment obtained from depth point cloud data was put forward. Then the special eye-under-finger structure to meet the needs of far-near combination was proposed, and, three key points of far-view point, near-view point and picking-point that determine the hand-eye path for far-near combination were defined. Finally, the far-near combined method composed of grape cluster detection in far view, far-near viewing switching and picking-point positioning based on depth data histogram in near view was established, which was realized by selecting the grape cluster bottom as the key clue. In field experiment of picking-point positioning in near view, the average running time of the algorithm is 0.29 s, and only 5 samples in total 100 failed to achieve accurate positioning. In lab experiments of far-near combined picking-point positioning, the success rate of accurate picking-point positioning reached 100%. This method is hopeful to overcome the deficiency of black-box processing of traditional image processing methods.

Automated summary

This paper proposes a novel method for accurate positioning of picking-point in table grape clusters using a combination of far-view and near-view depth data features. The method achieves high success rate through a combination strategy and a special eye-under-finger structure.