Quantitative softness and texture bimodal haptic sensors for robotic clinical feature identification and intelligent picking.

Journal: Science advances
PMID: 39047112

Abstract

Replicating human somatosensory networks in robots is crucial for dexterous manipulation, ensuring the appropriate grasping force for objects of varying softness and textures. Despite advances in artificial haptic sensing for object recognition, accurately quantifying haptic perceptions to discern softness and texture remains challenging. Here, we report a methodology that uses a bimodal haptic sensor to capture multidimensional static and dynamic stimuli, allowing for the simultaneous quantification of softness and texture features. This method demonstrates synergistic measurements of elastic and frictional coefficients, thereby providing a universal strategy for acquiring the adaptive gripping force necessary for scarless, antislippage interaction with delicate objects. Equipped with this sensor, a robotic manipulator identifies porcine mucosal features with 98.44% accuracy and stably grasps visually indistinguishable mature white strawberries, enabling reliable tissue palpation and intelligent picking. The design concept and comprehensive guidelines presented would provide insights into haptic sensor development, promising benefits for robotics.

Authors

  • Ye Qiu
    Department of Pharmacy, Changchun University of Chinese Medicine, Changchun, Jilin 130119, P.R. China.
  • Fangnan Wang
    College of Mechanical Engineering, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.
  • Zhuang Zhang
    Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan City, Shanxi Province, China.
  • Kuanqiang Shi
    College of Mechanical Engineering, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.
  • Yi Song
    Chongqing Three Gorges Central Hospital, China.
  • Jiutian Lu
    College of Mechanical Engineering, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.
  • Minjia Xu
    College of Mechanical Engineering, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.
  • Mengyuan Qian
    College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.
  • Wenan Zhang
    College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.
  • Jixuan Wu
  • Zheng Zhang
    Key Laboratory of Sustainable and Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, PR China.
  • Hao Chai
    Zhijiang College of Zhejiang University of Technology, Shaoxing, Zhejiang 312030, China.
  • Aiping Liu
    School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China.
  • Hanqing Jiang
    School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, China.
  • Huaping Wu
    College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China.

Keywords

External Resources

Popular Topics
Recent Journals