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Robotic human control sensors for robotic skin with both touch and vision
- Categories:Technical knowledge
- Time of issue:2021-09-03 15:19
(Summary description)For humans, the "sense of touch" plays a vital role in everyday activities. In tasks such as picking up objects, people use their sense of touch to feel whether they are hard or soft, light or heavy, warm or cold, and the combination of touch and sight allows us to avoid damaging them. In today's rapidly advancing technology, robots have been created that can walk, run, see, speak and hear. However, the study of robotic haptics is still relatively backward and new technologies need to be developed to overcome some of these problems.
Robotic human control sensors for robotic skin with both touch and vision
(Summary description)For humans, the "sense of touch" plays a vital role in everyday activities. In tasks such as picking up objects, people use their sense of touch to feel whether they are hard or soft, light or heavy, warm or cold, and the combination of touch and sight allows us to avoid damaging them. In today's rapidly advancing technology, robots have been created that can walk, run, see, speak and hear. However, the study of robotic haptics is still relatively backward and new technologies need to be developed to overcome some of these problems.
- Categories:Technical knowledge
- Time of issue:2021-09-03 15:19
- Views:
For humans, the "sense of touch" plays a vital role in everyday activities. In tasks such as picking up objects, people use their sense of touch to feel whether they are hard or soft, light or heavy, warm or cold, and the combination of touch and sight allows us to avoid damaging them. In today's rapidly advancing technology, robots have been created that can walk, run, see, speak and hear. However, the study of robotic haptics is still relatively backward and new technologies need to be developed to overcome some of these problems.
Multi-camera haptic sensors consist of four cameras that are located underneath a soft, transparent material that contains embedded scattered spherical particles. The cameras track the movement of these spherical particles, which are triggered by the deformation of the material when an external force is applied to it.
The researchers have also developed a machine learning (ML) architecture that analyses the motion of the spherical particles in the material. By analysing the motion of the spherical particles, the system can reconstruct the forces that cause the deformation of the material, also known as the contact force distribution.
The researchers explain, "We use relatively inexpensive cameras to generate a large amount of high-resolution image information, providing a total of about 65,000 pixels, which is important for a data-driven haptic sensor."
The multi-camera haptic sensor not only provides the total force value that most standard force sensors used on existing robots can, but also provides feedback on the distribution of all forces applied to its soft surface, thus decoupling the normal and tangential components. Due to its unique structural design, the new multi-camera haptic sensor has a larger contact surface and thinner structure than other camera-based haptic sensors because it does not require the addition of other reflective components, such as mirrors.
The use of multiple cameras allows the use of larger areas that can be covered by this type of tactile sensor in arbitrary shapes," the researchers said. This work also shows how data obtained on a subset of cameras can be transferred to other cameras, resulting in a way to obtain scalable data."
Robotic human control sensors can be extended to larger surfaces to create soft and sensable machine skins. The researchers discussed how their machine learning architecture could be adapted and optimised to facilitate its use on robots in the future.
For future intentions, the researchers said they now plan to extend the sensor's capabilities to reconstruct information about contact with complex and universally shaped objects. "We believe that sensing algorithms should always be developed with a data efficiency component in mind to facilitate widespread use in robotics, and therefore we will be moving in this direction in future work as well."
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