Tactile Sensing Mechanism For Soft Bionic Fingers
Researchers developed a tactile sensing mechanism that could be applied to robotic fingers made of soft materials.
Within the past decade, many researchers have focused on developing robotic systems that can artificially replicate the human sense of touch with soft materials based advanced bionic limbs and humanoid robots. However, soft materials are often unable to collect a wide range of sensory information. And, mimicking the complex biological sensing such as touch remains a challenge.
Researchers at Beihang University in Beijing have recently developed a new tactile sensing technique that could be applied to robotic fingers made of soft materials.
“The idea behind our recent paper is based on the proprioception framework found in humans, which is what determines our body position and load on our tendons/joints,” Chang Cheng, one of the researchers who carried out the study. “Think about when you put a blindfold on and cover your ears, you can still feel your hand posture, arm position, or how heavy a grocery bag is; this ability is known as proprioception. We have been working on a prosthetic hand research project and we are looking for ways to address the lack of sensory feedback in existing prosthetic hands.”
According to the researchers, industrial sensors are far more sensitive than human proprioceptors, but applying them to robotic fingers could help to gather more precise tactile sensory feedback. The prototype developed by the researchers consists of a tendon, a linear actuator, a strain sensor and a soft robotic finger.
“The tendon connects the finger to the actuator and the strain sensor is installed in the middle of the tendon,” Cheng said. “When the actuator is driven, it pulls the tendon, which causes the finger to bend/straighten, and the strain on the tendon changes accordingly. When the finger touches different objects, the sensor would output series of strain signals that characterize the touched objects.”
The researchers tested their developed prototype by running a series of tests, and they found that their technique could decipher the texture and stiffness with high levels of accuracy (100% and 99.7%, respectively).
“We are now exploring the slippage detection capabilities of this system,” Cheng said. “When we humans manipulate or grasp things, slippage is almost unavoidable, therefore detection and control of slippage is crucial to robust and reliable controls. So, we believe slippage detection would be a nice feature to add, and our preliminary experiments showed really promising results.”
More information regarding the research can be found here.