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Republicată de Platon One of the hottest topics in robotics is the field of soft robots, which utilizes squishy and flexible materials rather than traditional rigid materials.

But soft robots have been limited due to their lack of good sensing. A good robotic gripper needs to feel what it is touching tactile sensingand it needs to sense the positions of its fingers proprioception.

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Such sensing has been missing from most soft robots. Schwarzman College of Computing.

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When classifying objects, the sensors correctly identified 10 objects with over 90 percent accuracy, even when an object slipped out of grip. The gripper, which looks much like a two-finger cup gripper you might see at a soda station, uses a tendon-driven mechanism to actuate the fingers.

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When tested on metal objects of various shapes, the system had over 96 percent recognition accuracy. When a vacuum is applied to the balloon, the origami structure closes around the object, indicator de opțiuni binare trx the gripper deforms to its structure.

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While this motion lets the gripper grasp a much wider range of objects than ever before, such as soup cans, hammers, wine glasses, drones, and even a single broccoli floret, the greater intricacies of delicacy and understanding were still out of reach — until they added the sensors. When the sensors experience force or strain, the internal pressure changes, and the team can measure this change in pressure to identify when it will feel that again.

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In addition to the latex sensor, the team also developed an algorithm which uses feedback to let the gripper possess a human-like duality of being both strong and precise — and 80 percent of the tested objects were successfully grasped without damage.

The team tested the gripper-sensors on a variety of household tranzacționarea roboților pe rețele neuronale, ranging from heavy bottles to small, delicate objects, including cans, apples, a toothbrush, a water bottle, and a bag of cookies.

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Going forward, the team hopes to make the methodology scalable, using computational design and reconstruction methods to improve the resolution and coverage using this new sensor technology.

Eventually, they imagine using the new sensors to create a fluidic sensing skin that shows scalability and sensitivity. Hughes co-wrote the new paper with Rus, which they will present virtually at the International Conference on Robotics and Automation.

Soft fingers allow a wide range of deformations, but to be used in a controlled way there must be rich tactile and proprioceptive sensing. To create GelFlex, the team used silicone material to fabricate the soft and transparent finger, and put one camera near the fingertip and the other in the middle of the finger.

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Then, they painted reflective ink on the front and side surface of the finger, and added LED lights on the back. This allows the internal fish-eye camera to observe the status of the front and side surface of the finger. The team trained neural networks to extract key information from the internal cameras for feedback. One neural net was trained to predict the bending angle of GelFlex, and the other was trained to estimate the shape and size of the objects being grabbed.

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During testing, the average positional error while gripping was less than 0. In a second set of tests, the gripper was challenged with grasping and recognizing cylinders and boxes of various sizes.

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Out of 80 trials, only three were classified incorrectly. In the future, the team hopes to improve the proprioception and tactile sensing algorithms, and utilize vision-based sensors to estimate more complex finger configurations, such as twisting or lateral bending, which are challenging for common sensors, but should be attainable with embedded cameras. They will tranzacționarea roboților pe rețele neuronale the paper virtually at the International Conference on Robotics and Automation.

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