Sensor for robots mimicks skin characteristics

Researchers have developed a new soft tactile sensor with skin-comparable characteristics. A robotic gripper with the sensor mounted at the fingertip could accomplish challenging tasks such as stably grasping fragile objects and threading a needle. Their research provided new insight into tactile sensor design and could contribute to various applications in the robotics field, such as smart prosthetics and human-robot interaction.

A main characteristic of human skin is its ability to sense the shear force, meaning the force that makes two objects slip or slide over each other when coming into contact. By sensing the magnitude, direction and the subtle change of shear force, our skin can act as feedback and allow us to adjust how we should hold an object stably with our hands and fingers or how tight we should grasp it.

To mimick this important feature of human skin, Dr. Shen Yajing, Associate Professor at CityU's Department of Biomedical Engineering (BME=, and Dr. Pan Jia, a collaborator from the University of Hong Kong (HKU), have developed a novel, soft tactile sensor. The sensor is in a multi-layered structure like human skin and includes a flexible and specially magnetized film of about 0.5mm thin as the top layer. When an external force is exerted on it, it can detect the change of the magnetic field due to the film's deformation. More importantly, it can "decouple," or decompose, the external force automatically into two components—normal force (the force applied perpendicularly to the object) and shear force, providing the accurate measurement of these two forces respectively.

"It is important to decouple the external force because each force component has its own influence on the object. And it is necessary to know the accurate value of each force component to analyze or control the stationary or moving state of the object," explained Yan Youcan, Ph.D. student at BME and the first author of the paper.

Deep learning enhanced accuracy

Moreover, the senor possesses another human skin-like characteristic—the tactile "super-resolution" that allows it to locate the stimuli's position as accurate as possible. "We have developed an efficient tactile super-resolution algorithm using deep learning and achieved a 60-fold improvement of the localisation accuracy for contact position, which is the best among super-resolution methods reported so far," said Dr. Shen. Such an efficient tactile super-resolution algorithm can help improve the physical resolution of a tactile sensor array with the least number of sensing units, thus reducing the number of wirings and the time required for signal transmitting.

"To the best of our knowledge, this is the first tactile sensor that achieved self-decoupling and super-resolution abilities simultaneously," he added.

Robotic hand with the new sensor completes challenging tasks

By mounting the sensor at the fingertip of a robotic gripper, the team showed that robots can accomplish challenging tasks. For example, the robotic gripper stably grasped fragile objects like an egg while an external force trying to drag it away, or threaded a needle via teleoperation. "The super-resolution of our sensor helps the robotic hand to adjust the contact position when it grasps an object. And the robotic arm can adjust force magnitude based on the force decoupling ability of the tactile sensor," explained Dr. Shen.

He added that the sensor can be easily extended to the form of sensor arrays or even continuous electronic skin that covers the whole body of the robot in the future. The sensitivity and measurement range of the sensor can be adjusted by changing the magnetisation direction of the top layer (magnetic film) of the sensor without changing the sensor's thickness. This enabled the e-skin to have different sensitivity and measurement range in different parts, just like human skin.

Also, the sensor has a much shorter fabrication and calibration processes compared with other tactile sensors, facilitating the actual applications. "This proposed sensor could be beneficial to various applications in the robotics field, such as adaptive grasping, dextrous manipulation, texture recognition, smart prosthetics and human-robot interaction. The advancement of soft artificial tactile sensors with skin-comparable characteristics can make domestic robots become part of our daily life," concluded Dr. Shen.

The findings were published in Science Robotics.

Subscribe to our newsletter

Related articles

Algorithm designs soft robots that sense

Algorithm designs soft robots that sense

Deep learning technique optimizes the arrangement of sensors on a robot’s body to ensure efficient operation.

A deep learning e-skin decodes complex human motion

A deep learning e-skin decodes complex human motion

A deep learning powered single-strained electronic skin sensor can capture human motion from a distance.

3D printed origami technology to fight Covid-19

3D printed origami technology to fight Covid-19

Researchers are replicating the subtle folding of origami to create 3D printable technologies to aid in the fight against COVID-19.

Deep learning-based holographic point-of-care sensor

Deep learning-based holographic point-of-care sensor

Researchers have developed a rapid and cost-effective particle agglutination based sensor that is powered by holographic imaging and deep learning

Disinfection robot lends hospitals a big hand

Disinfection robot lends hospitals a big hand

Researchers have developed a smart functional robot that realized simultaneous disinfection of both air and object surface.

Robotic neck brace to analyze cancer treatment impacts

Robotic neck brace to analyze cancer treatment impacts

Researchers have developed a robotic neck brace that may help doctors analyze the impact of cancer treatments on the neck mobility of patients and guide their recovery.

A 3D printed multifunctional pressure sensor

A 3D printed multifunctional pressure sensor

The 3D printed pressure sensor embedded with a temperature sensor is low-cost and scalable to large-scale production of smart robotic systems.

When the robot smiles back

When the robot smiles back

Researchers use AI to teach robots to make appropriate reactive human facial expressions, an ability that could build trust between humans and their robotic co-workers and care-givers.

A new framework for machine speech translation

A new framework for machine speech translation

Researchers propose a deep learning-based model for mimicking and continuously modifying speaker voice identity during speech translation.

Popular articles

Photo

The “RoboWig” untangle your hair

Nurses typically spend 18 to 40 percent of their time performing direct patient care tasks, oftentimes for many patients and with little time to spare. Personal care robots that brush your hair could provide substantial help and relief.

Subscribe to Newsletter