Summary Analysis Draft 3

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Soft Robotic Arm uses flexible sensors to understand its position

MEC 1281

Summary_Analysis

Draft #3

By Sim Wei Yan

21st Feb 2021

In the article, “Soft robotic arm...” (2020), Matheson described Ryan Truby, a postdoc in the MIT Computer Science and Artificial Laboratory. Truby, empowering 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. This is due to its infinite number of movements at any time and uses its own flexible sensors to receive feedbacks for control instead of vision system to provide feedbacks. Therefore, it creates a 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 is unable to capture precise movements due to its infinite movements, it still provides a stepping stone for machine learning in soft robotics control, enabling the experts to explore new models with improved sensitivity and control applications. 

On the other hand, soft robotic arm is more seemly than traditional rigid robot in the real-world application, but soft robot sensing technology have yet to be polished since it is a new field. The future aim for this technology is to develop a soft robot that is proficient and capable of handling objects in the environment. 

However, one key problem for the preliminary sensors in the soft robotic arm, it is unable to capture precise data due to movement of the robotic arm. 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. Due to the infinite movements and the flexibility of the soft robotic arm, it will result in changes of the characteristics of a soft sensor attached on it. Hence, affecting the sensitivity of the sensor when there is change in strain and pressure. In addition, the article stated it is impracticable to implement thousands of sensors in the soft robotic system due to problems in the components such as the electronics, sensor and space. Thus, the total number of sensors can be reduced by implementing the sensors at the best location for detecting multiple deformation. 

Other key elements must be taken into considerations as well, such as the contact behaviour in between a sensorized soft robotic and an object that are difficult to anticipate as friction is involved. Modelling of the sensor would be much more sophisticated, involving advanced method with high cost and the outcome might not corresponds with physical systems. 

Secondly, the soft robots have incapability in its control application, thus an approach "finite element methods (FEMs)"(Wang, H., et al, 2018, p. 31) have been developed to introduce "real-time control algorithms"(Wang, H., et al, 2018, p. 31) as stated in the article. Integrating the soft sensors is very beneficial, enabling more complicated task and more precise control of the soft robot. The sensory responses have to be evaluated for a range of robotic tasks in function of the actuation mechanism, as well as depending on the scenario in which the robot is moving. 

Taking everything into account, soft robotic sensing is still its beginning phase and there are many challenges to overcome towards autonomous soft robots. These challenges could inspire new ideas for innovative solutions towards perceptive soft robots through scientific communities. Thus, a potential model may be feasible in the foreseeable future.

Reference list:

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

Zhongkai Zhang, Dequidt, J., & Duriez, C. (2018). Vision-Based Sensing of External Forces Acting on Soft Robots Using Finite Element Method. IEEE Robotics and Automation Letters, 3(3), 1529–1536. https://doi.org/10.1109/LRA.2018.2800781


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