Branch interference sensing and handling by tactile enabled robotic apple harvesting

Hongyu Zhou, Hanwen Kang, Xing Wang, Wesley Au, Michael Wang, Chao Chen

Research output: Contribution to journalArticleResearchpeer-review


In the dynamic and unstructured environment where horticultural crops grow, obstacles and interference frequently occur but are rarely addressed, which poses significant challenges for robotic harvesting. This work proposed a tactile-enabled robotic grasping method that combines deep learning, tactile sensing, and soft robots. By integrating fin-ray fingers with embedded tactile sensing arrays and customized perception algorithms, the robot gains the ability to sense and handle branch interference during the harvesting process and thus reduce potential mechanical fruit damage. Through experimental validations, an overall 83.3–87.0% grasping status detection success rate, and a promising interference handling method have been demonstrated. The proposed grasping method can also be extended to broader robotic grasping applications wherever undesirable foreign object intrusion needs to be addressed.
Original languageEnglish
Article number503
Number of pages16
Issue number2
Publication statusPublished - 9 Feb 2023

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