Flexible robots that can change color

Physics Today ◽  
2012 ◽  
Keyword(s):  
2020 ◽  
pp. 421-454
Author(s):  
K. Desoyer ◽  
P. Lugner ◽  
I. Troch ◽  
P. Kopacek

Author(s):  
Alessandro De Luca
Keyword(s):  

2006 ◽  
Vol 11 (6) ◽  
pp. 690-698 ◽  
Author(s):  
Shuzhi Sam Ge ◽  
Keng Peng Tee ◽  
Ivan E. Vahhi ◽  
Francis E. H. Tay

Author(s):  
Hidenori Murakami ◽  
Oscar Rios ◽  
Takeyuki Ono

For actuator design and motion simulations of slender flexible robots, planar C1-beam elements are developed for Reissner’s large deformation, shear-deformable, curbed-beam model. Internal actuation is mechanically modeled by a rate-form of beam constitutive relation, where actuation curvature is prescribed at each time. Geometrically, a curbed beam is modeled as a frame bundle, whereby at each point on beam’s curve of centroids a moving orthonormal frame is attached to a cross section. After a finite element discretization, a curve of centroids is modeled as a C1-curve, employing cubic shape functions for both planar coordinates with an arc-parameter. The cubic shape functions have already been utilized in linear Euler-Bernoulli beams for the interpolation of transverse displacement. To define the rotation angle of each cross section or the attitude of the moving frame, quadratic shape functions are used introducing a middle node, resulting in three angular nodal displacements. As a result, each beam element has total eleven nodal coordinates. The implementation of a nonlinear finite element code is facilitated by the principle of virtual work, which yields Reissner’s large deformation curbed beam model as the Euler-Lagrange equations. For time integration, the Newmark method is utilized. Finally, as applications of the code, a few inchworm motions induced by different actuation curvature fields are presented.


Robotics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 93
Author(s):  
Sarthak Bhagat ◽  
Hritwick Banerjee ◽  
Zion Tsz Ho Tse ◽  
Hongliang Ren

The authors wish to make the following corrections to this paper [1]: In Figure 1 of this paper [...]


Robotics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Sarthak Bhagat ◽  
Hritwick Banerjee ◽  
Zion Ho Tse ◽  
Hongliang Ren

The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics.


Sign in / Sign up

Export Citation Format

Share Document