TY - JOUR
T1 - Skin-Inspired Triple Tactile Sensors Integrated on Robotic Fingers for Bimanual Manipulation in Human-Cyber-Physical Systems
AU - Zhao, Shumi
AU - Li, Zhijun
AU - Xia, Haisheng
AU - Cui, Rongxin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2025
Y1 - 2025
N2 - Collaborative robots are predicted to interact physically with humans in human-cyber-physical systems (HCPSs). Robotic hands are able to record force and temperature simultaneously through soft and conformable sensors applied over finger surfaces and conforming to the complex curved geometries of automatic machines with tactile perception. Here, skin-inspired triple tactile (SITT) sensors are integrated into robotic fingers to enable precise bimanual grasping. The SITT sensor has skin-inspired multilayer microstructures, which integrate three sensors, namely, an interdigital electrode sensor, a flexible force sensor, and a temperature sensor. The SITT sensor can simultaneously or independently measure a material's dielectric property, tactile force and temperature. An HCPS based on SITT sensors, a data acquisition board, bimanual robotic hands, and human-robot interaction software is developed for efficient bimanual manipulation. Through 3C assembly experiments, the designed HCPS is demonstrated to execute complex tasks. This research presents a novel methodology for constructing robust tactile sensors for robotic fingers in a bimanual manipulation system, and it presents significant potential across various aspects of intelligent production, including sensitive object handling, adaptive manipulation, and interactive robotics applications. Note to Practitioners - This work aims to overcome the challenge of skin-inspired triple tactile (SITT) sensors developed for robotic fingers in a human-robot collaborative assembly scenario, which can also be used in many other similar human-robot/machine collaborations (e.g., wearable prosthetics with tactile feedback) with practical value. Its capability to accurately measure the touched objects' information (e.g., material dielectric property, force, temperature) is crucial for the bimanual robot to successfully interact with human operators. HCPS is valuable for its potential to achieve complicated interactions among humans, cyber systems, and physical resources. Collaborative robots with tactile sensors are expected to interact physically with humans in the HCPS. In this paper, we report SITT sensors integrated on robotic fingers to enable precise bimanual grasping. They can simultaneously or independently measure material dielectric properties and tactile forces and record temperature. By combining tactile perception information and a related control method, our smart bimanual robotic hands with the developed HCPS demonstrates the capability to execute 3C assembly in intelligent manufacturing. In the future, additional application scenarios will be designed for the HCPS platform, and some traditional tasks (such as a single arm for collaborative assembly) will be replaced by our hardware and software systems.
AB - Collaborative robots are predicted to interact physically with humans in human-cyber-physical systems (HCPSs). Robotic hands are able to record force and temperature simultaneously through soft and conformable sensors applied over finger surfaces and conforming to the complex curved geometries of automatic machines with tactile perception. Here, skin-inspired triple tactile (SITT) sensors are integrated into robotic fingers to enable precise bimanual grasping. The SITT sensor has skin-inspired multilayer microstructures, which integrate three sensors, namely, an interdigital electrode sensor, a flexible force sensor, and a temperature sensor. The SITT sensor can simultaneously or independently measure a material's dielectric property, tactile force and temperature. An HCPS based on SITT sensors, a data acquisition board, bimanual robotic hands, and human-robot interaction software is developed for efficient bimanual manipulation. Through 3C assembly experiments, the designed HCPS is demonstrated to execute complex tasks. This research presents a novel methodology for constructing robust tactile sensors for robotic fingers in a bimanual manipulation system, and it presents significant potential across various aspects of intelligent production, including sensitive object handling, adaptive manipulation, and interactive robotics applications. Note to Practitioners - This work aims to overcome the challenge of skin-inspired triple tactile (SITT) sensors developed for robotic fingers in a human-robot collaborative assembly scenario, which can also be used in many other similar human-robot/machine collaborations (e.g., wearable prosthetics with tactile feedback) with practical value. Its capability to accurately measure the touched objects' information (e.g., material dielectric property, force, temperature) is crucial for the bimanual robot to successfully interact with human operators. HCPS is valuable for its potential to achieve complicated interactions among humans, cyber systems, and physical resources. Collaborative robots with tactile sensors are expected to interact physically with humans in the HCPS. In this paper, we report SITT sensors integrated on robotic fingers to enable precise bimanual grasping. They can simultaneously or independently measure material dielectric properties and tactile forces and record temperature. By combining tactile perception information and a related control method, our smart bimanual robotic hands with the developed HCPS demonstrates the capability to execute 3C assembly in intelligent manufacturing. In the future, additional application scenarios will be designed for the HCPS platform, and some traditional tasks (such as a single arm for collaborative assembly) will be replaced by our hardware and software systems.
KW - 3C products assembly
KW - Tactile sensor
KW - bimanual robotic hands
KW - human-cyber-physical system
KW - multimodal sensor
UR - http://www.scopus.com/inward/record.url?scp=85174840217&partnerID=8YFLogxK
U2 - 10.1109/TASE.2023.3320710
DO - 10.1109/TASE.2023.3320710
M3 - 文章
AN - SCOPUS:85174840217
SN - 1545-5955
VL - 22
SP - 656
EP - 666
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
ER -