Summary Analysis Draft 1

In the article, “Soft robotic arm...” (2020), Matheson described Ryan Truby, a postdoc in the MIT Computer Science and Artificial Laboratory. Truby, along with other MIT researchers have empowered a malleable robotic arm to operate by utilizing motion and data in 3D environment with its own sensorized skin. 

Matheson mentioned about Truby's opinions, whereby soft robots are more advantageous compared to traditional rigid design due to its infinite number of movements at any time, and also uses its own flexible sensors to receive feedbacks for control instead of vision system to provide feedbacks. Along with the limitation in the control applications and train planning process due to its infinite number of movements in soft robot. In the article, Matheson also depicted Truby's statement with regards to the researchers' future aim, using the soft robotic arm "to orient and control themselves automatically, to pick things up and interact with the world"(Ryan Truby,n.d,Paragraph 6) as well as, to progress onto artificial limbs that can dexterously control in the environment.

Even though the soft sensor are unable to capture precise movements, it still provides as a stepping stone for machine learning in soft robotics control, enabling the experts to explore improved sensitivity and develop new models. 

In the article, "Toward Perceptive...", (2018) the article mentioned several computational approaches have been successfully implemented into the traditional robotics design such as the microelectromechanical system-based sensors. But, it is a challenge in soft robotics design due to the infinite movements and the flexibility of the soft robotic arm that will result in changes of the characteristics of a soft sensor attached on it. Thus, affecting the sensitivity of the sensor when there is change in strain and pressure. 

It is impracticable to implement thousands of sensors in a soft robotic system due to the problems in various components such as wiring, electronics, sensor size and space. The total number of sensors can be reduced by implementing the sensors at the best location for detecting multiple deformation. More over, other key elements have to be taken into considerations as well, such as the contact behavior in between a sensorized soft robotic and an object are difficult to anticipate as friction is involved. Modeling of the sensor would be much more sophisticated, involving advanced method with high cost and the outcome might not corresponds with physical systems. 

The article further stated, "finite element methods (FEMs)" have been developed to simulate soft robot movements and to introduce "real-time control algorithms". Integrating the soft sensors is very beneficial, enabling more complicated task and more precise control of the soft robot.

Matheson, R (2020, 16 Feb). 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/

Wang, H., Totaro, M., & Beccai, L. (2018, 13 Jul). Toward Perceptive Soft Robots: Progress and Challenges. Advanced Science, 5(9), 1800541–n/a. https://doi.org/10.1002/advs.201800541