  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.
