scholarly journals Control Space Reduction and Real-Time Accurate Modeling of Continuum Manipulators Using Ritz and Ritz–Galerkin Methods

2018 ◽  
Vol 3 (1) ◽  
pp. 328-335 ◽  
Author(s):  
S. M. Hadi Sadati ◽  
S. Elnaz Naghibi ◽  
Ian D. Walker ◽  
Kaspar Althoefer ◽  
Thrishantha Nanayakkara
2018 ◽  
Vol 15 (05) ◽  
pp. 1850021 ◽  
Author(s):  
Seri M. Mustaza ◽  
Chakravarthini M. Saaj ◽  
Francisco J. Comin ◽  
Wissam A. Albukhanajer ◽  
Duale Mahdi ◽  
...  

Tunable stiffness control is critical for undertaking surgical procedures using soft manipulators. However, active stiffness control in soft continuum manipulators is very challenging and has been rarely realized for real-time surgical applications. Low stiffness at the tip is much preferred for safe navigation of the robot in restricted spaces inside the human body. On the other hand, high stiffness at the tip is demanded for efficiently operating surgical instruments. In this paper, the manipulability and characteristics of a class of soft hyper-redundant manipulator, fabricated using Ecoflex-0050[Formula: see text] silicone, is discussed and a new methodology is introduced to actively tune the stiffness matrix, in real-time, for disturbance rejection and stiffness control. Experimental results are used to derive a more accurate description of the characteristics of the soft manipulator, capture the varying stiffness effects of the actuated arm and consequently offer a more accurate response using closed loop feedback control in real-time. The novel results presented in this paper advances the state-of-the-art of tunable stiffness control in soft continuum manipulators for real-time applications.


Robotica ◽  
2014 ◽  
Vol 34 (7) ◽  
pp. 1566-1586 ◽  
Author(s):  
Jinglin Li ◽  
Jing Xiao

SUMMARYA continuum manipulator, such as a multisection trunk/tentacle robot, performs manipulation tasks by continuously deforming into different concave shapes. While such a robot is promising for manipulating a wide range of objects in less-structured and cluttered environments, it poses a greater challenge to collision detection than conventional, articulated manipulators. Existing collision detection algorithms are built upon intersection checking between convex primitives, such as between two convex polygons or polyhedra, with the assumption that both the manipulator and the objects in the environment are modeled in terms of those primitives, for example, as polygonal meshes. However, to approximate a continuum manipulator with a polygonal mesh requires a fine mesh because of its concavity, and each time the manipulator changes its configuration by deforming its shape, the mesh has to be updated for the new configuration. This makes mesh-based collision detection involving such a robot much more computationally expensive than that involving an articulated manipulator with rigid links.Hence, we introduce an efficient algorithm for Collision Detection between a Continuum Manipulator (CD-CoM) and its environment based on analytical intersection checking with nonconvex primitives. Our algorithm applies to the exact model of any continuum manipulator consisting of multiple uniform-curvature sections of toroidal and (sometimes) cylindrical shapes as well as more general continuum manipulators whose sections can be approximated by toroidal and cylindrical primitives. Our test results show that using this algorithm is both more accurate and efficient in time and space to detect collisions than approximating a continuum manipulator as a polygonal mesh. Moreover, the CD-CoM algorithm also provides the minimum distance information between the continuum manipulator and objects when there is no collision. Such an efficient algorithm is essential for path/trajectory planning of continuum manipulators in real-time.


Author(s):  
Cheol Oh ◽  
Stephen G. Ritchie

One of the fundamental requirements for facilitating implementation of any advanced transportation management and information system (ATMIS) is the development of a real-time traffic surveillance system able to produce reliable and accurate traffic performance measures. This study presents a new framework for anonymous vehicle tracking capable of tracing individual vehicles by the vehicle features. The core part of the proposed vehicle tracking method is a vehicle reidentification algorithm for signalized intersections based on inductive vehicle signatures. The new vehicle reidentification system consists of two major components: search space reduction and probabilistic pattern recognition. Not only real-time intersection performance but also intersection origin–destination information can be obtained as the algorithm’s basic output. A systematic simulation investigation was conducted of the performance and feasibility of anonymous vehicle tracking on signalized arterials using the Paramics simulation model. Extensive research experience with vehicle reidentification techniques on single roadway segments was the basis for investigating the performance that could be obtained from tracking individual vehicles across multiple detector stations. The findings of this study serve as a logical and necessary precursor to possible field implementation of vehicle reidentification techniques. The proposed anonymous vehicle tracking methodology with existing traffic surveillance infrastructure would be an invaluable tool for operating agencies in support of ATMIS strategies for congestion monitoring, adaptive traffic control, system evaluation, and provision of real-time traveler information.


2021 ◽  
Vol 8 ◽  
Author(s):  
Abbas Tariverdi ◽  
Venkatasubramanian Kalpathy Venkiteswaran ◽  
Michiel Richter ◽  
Ole J. Elle ◽  
Jim Tørresen ◽  
...  

This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.


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