scholarly journals Review on generic methods for mechanical modeling, simulation and control of soft robots

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0251059
Author(s):  
Pierre Schegg ◽  
Christian Duriez

In this review paper, we are interested in the models and algorithms that allow generic simulation and control of a soft robot. First, we start with a quick overview of modeling approaches for soft robots and available methods for calculating the mechanical compliance, and in particular numerical methods, like real-time Finite Element Method (FEM). We also show how these models can be updated based on sensor data. Then, we are interested in the problem of inverse kinematics, under constraints, with generic solutions without assumption on the robot shape, the type, the placement or the redundancy of the actuators, the material behavior… We are also interested by the use of these models and algorithms in case of contact with the environment. Moreover, we refer to dynamic control algorithms based on mechanical models, allowing for robust control of the positioning of the robot. For each of these aspects, this paper gives a quick overview of the existing methods and a focus on the use of FEM. Finally, we discuss the implementation and our contribution in the field for an open soft robotics research.

2019 ◽  
Vol 5 (1) ◽  
pp. 409-411
Author(s):  
Tobias Salesch ◽  
Jonas Gesenhues ◽  
Dirk Abel

AbstractThis paper deals with the design, simulation and control of a new lightweight hybrid Mock-Loop (MCL) concept. The proof of concept is evaluated by two simulation approaches. First, the design parameters are chosen by an optimal control problem. Second, a cascading controller structure is evaluated in a simulation. Both show that with a suitable range of the design parameter the new lightweight concept can be used as a MCL. To validate these findings, further investigations with the MCL under realistic test conditions are required.


2017 ◽  
Vol 31 (22) ◽  
pp. 1208-1224 ◽  
Author(s):  
E. Coevoet ◽  
T. Morales-Bieze ◽  
F. Largilliere ◽  
Z. Zhang ◽  
M. Thieffry ◽  
...  

Author(s):  
Ramya M V ◽  
Purushothama G K ◽  
Prakash K R

<p class="Default">This article describes the design and implementation of a remote laboratory for learning sensors based experiments and its applications using embedded systems and Internet of Things (IoT) platform. The main objective of this remote laboratory is to enhance the learning on sensors in engineering education and dealing with the industrial automation applications. With the growing IoT platform for automation, the proposed system can monitor the sensor data and allows the learner to work from anywhere and anytime using mobile android application. Thus, learners can develop knowledge on sensors and control algorithms required for the automation industries and then deploy them on the real industrial automation modules.</p>


Author(s):  
Ben Pawlowski ◽  
Charles W. Anderson ◽  
Jianguo Zhao

Abstract Soft robots made from soft materials recently attracted tremendous research owing to their unique softness compared with rigid robots, making them suitable for applications such as manipulation and locomotion. However, also due to their softness, the modeling and control of soft robots present a significant challenge because of the infinite degree of freedom. In this case, although analytic solutions can be derived for control, they are too computationally intensive for real-time application. In this paper, we aim to leverage reinforcement learning to approach the control problem. We gradually increase the complexity of the control problems to learn. We also test the effectiveness and efficiency of reinforcement learning techniques to the control of soft robots for different tasks. Simulation results show that the control commands to be computed in milliseconds, allowing effective control of soft manipulators, up to trajectory tracking.


2017 ◽  
Vol 16 (3) ◽  
pp. 587-595
Author(s):  
Vasile Mircea Cristea ◽  
Ph.m Thai Hoa ◽  
Mihai Mogos-Kirner ◽  
Csavdari Alexandra ◽  
Paul Serban Agachi

2019 ◽  
Vol 67 (4) ◽  
pp. 315-329
Author(s):  
Rongjiang Tang ◽  
Zhe Tong ◽  
Weiguang Zheng ◽  
Shenfang Li ◽  
Li Huang

2019 ◽  
Author(s):  
Ujwal Shirode ◽  
Aishwarya Aher ◽  
Pallavi Bale ◽  
Aishwarya Kadam

Sign in / Sign up

Export Citation Format

Share Document