2021/10/18 by Hongyu Zhou, Xing Wang, Zhou, Hongyu +5
Agricultural and Biological Sciences · Engineering · #FOS: Computer and information sciences #Modular Robots and Swarm Intelligence #Robotics (cs.RO) #Smart Agriculture and AI #Soft Robotics and Applications
paper · pdf · doi:10.48550/arxiv.2110.09051
openalex publication_date 2021/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.