A Tactile-enabled Grasping Method for Robotic Fruit Harvesting
This addresses the challenge of obstacle interference in robotic crop harvesting, which is a domain-specific problem for agriculture automation, but it appears incremental as it builds on existing deep learning and tactile sensing approaches.
The paper tackled the problem of foreign object intrusion in robotic fruit harvesting by introducing a tactile-enabled grasping method that combines deep learning with low-cost tactile sensing on a multi-DoF soft gripper, demonstrating promising performance in distinguishing grasping scenarios and adaptability in picking.
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.