Design summary and thesis draft 2

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According to the article “Soft robotic arm...” (Matheson, 2020), researchers from the Massachusetts Institute of Technology (MIT) have developed a system comprising of sensors with high compliance and an information processing model that allows a robot equipped with these to know it’s position in 3D space efficiently. The sensors are wrapped all around the robot’s body like skin. Traditionally, robots get their configuration info from a complex system of cameras, which are unfeasible for actual soft robot applications. “We’re sensorizing soft robots to get feedback for control from sensors, not vision systems, using a very easy, rapid method for fabrication,” said Truby. Researchers also said that the entire system is still inadequate, but it is a crucial foothold from which advances in soft robotic control can be made. Moving ahead, researchers are looking at designing better sensors and learning models to cut down on training for new robots.


Although the soft robotic arm is a better alternative to the traditional robots, its development has yet to be perfected. Researchers are seeking ways to improve on the arm's effectiveness

 

Matheson, R. (2020, February 16). Soft robotic arm uses flexible sensors to understand its position. Control Engineering. https://www.controleng.com/articles/soft-robotic-arm-uses-flexible-sensors-to-understand-its-position/nd its position

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summary and analysis Draft 1

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According to the article “Soft robotic arm...” (Matheson, 2020), researchers from the Massachusetts Institute of Technology (MIT) have developed a system comprising of sensors with high compliance and an information processing model that allows a robot equipped with these to know it’s position in 3D space efficiently. The sensors are wrapped all around the robot’s body like skin. Traditionally, robots get their configuration info from a complex system of cameras, which are unfeasible for actual soft robot applications. Ryan Truby, one of the researchers, said that “We’re sensorizing soft robots to get feedback for control from sensors, not vision systems, using a very easy, rapid method for fabrication.”. He also said that the entire system is still inadequate, but it is a crucial foothold from which advances in soft robotic control can be made. Moving ahead, researchers are looking at designing better sensors and learning models to cut down on training for new robots. Although the sof
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