A Tactile-enabled Grasping Method for Robotic Fruit Harvesting

TL;DR

This paper introduces an innovative tactile-enabled robotic grasping method that combines deep learning and tactile sensing on a soft gripper to improve obstacle handling in fruit harvesting.

Contribution

It is the first to integrate deep learning with tactile sensing hardware for obstacle-aware robotic grasping in crop harvesting.

Findings

Demonstrated effective obstacle discrimination in grasping scenarios

Achieved high adaptability with a 4-finger soft gripper

Showed promising experimental results in real harvesting tasks

Abstract

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.