In the robotic crop harvesting environment, foreign objects intrusion in the gripper workspace is frequently occurring and unignorable, however, rarely addressed. This paper presents a novel intelligent robotic grasping method capable of handling obstacle interference, which is the first of its kind in the literature. The proposed method combines deep learning algorithms with low-cost tactile sensing hardware on a multi-DoF soft robotic gripper. Through experimental validations, the proposed method demonstrated promising performance in distinguishing various grasping scenarios. The 4-finger independently controlled gripper presented outstanding adaptability to handle various picking scenarios. The overall performance of this work indicated great potential for solving the robotic fruit harvesting challenges.
@article{arxiv.2110.09051,
title = {A Tactile-enabled Grasping Method for Robotic Fruit Harvesting},
author = {Hongyu Zhou and Xing Wang and Hanwen Kang and Chao Chen},
journal= {arXiv preprint arXiv:2110.09051},
year = {2021}
}