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Keywords = double SCARA

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26 pages, 7924 KiB  
Article
Application of Barycentric Coordinates and the Jacobian Matrix to the Analysis of a Closed Structure Robot
by Ivan Chavdarov
Robotics 2024, 13(10), 152; https://doi.org/10.3390/robotics13100152 - 12 Oct 2024
Cited by 2 | Viewed by 1818
Abstract
A new approach is presented to study the kinematic properties of stationary robots with a closed structure. It combines the application of conventional methods from kinematics with geometric parameters represented in a barycentric coordinate system. This allows examining the influence of the proportions [...] Read more.
A new approach is presented to study the kinematic properties of stationary robots with a closed structure. It combines the application of conventional methods from kinematics with geometric parameters represented in a barycentric coordinate system. This allows examining the influence of the proportions of the robot’s links on its basic mechanical characteristics. Each point from the newly introduced barycentric space corresponds to a set of robots with the same link proportions. The proposed approach is used to study three aspects: the link proportions for which the robot can exist; the shape of the robot’s workspace; and the possible singular configurations. This is valuable when evaluating the qualities of existing robots and could be applied to the design of new mechanical systems. An example of a 5-link robot with a closed structure is considered. The conditions for the existence of the mechanism and the conditions under which certain types of singular configurations can occur are defined. The example reveals the great potential of combining barycentric coordinates and Jacobian properties. The barycentric coordinates of 10 robots with a 5-link closed structure known from the literature are determined, and their properties are analyzed. The results are presented graphically. An extension of the application area of the approach is discussed. Full article
(This article belongs to the Section Intelligent Robots and Mechatronics)
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13 pages, 459 KiB  
Article
Compound Heuristic Information Guided Policy Improvement for Robot Motor Skill Acquisition
by Jian Fu, Cong Li, Xiang Teng, Fan Luo and Boqun Li
Appl. Sci. 2020, 10(15), 5346; https://doi.org/10.3390/app10155346 - 3 Aug 2020
Cited by 4 | Viewed by 2481
Abstract
Discovering the implicit pattern and using it as heuristic information to guide the policy search is one of the core factors to speed up the procedure of robot motor skill acquisition. This paper proposes a compound heuristic information guided reinforcement learning algorithm PI [...] Read more.
Discovering the implicit pattern and using it as heuristic information to guide the policy search is one of the core factors to speed up the procedure of robot motor skill acquisition. This paper proposes a compound heuristic information guided reinforcement learning algorithm PI2-CMA-KCCA for policy improvement. Its structure and workflow are similar to a double closed-loop control system. The outer loop realized by Kernel Canonical Correlation Analysis (KCCA) infers the implicit nonlinear heuristic information between the joints of the robot. In addition, the inner loop operated by Covariance Matrix Adaptation (CMA) discovers the hidden linear correlations between the basis functions within the joint of the robot. These patterns which are good for learning the new task can automatically determine the mean and variance of the exploring perturbation for Path Integral Policy Improvement (PI2). Compared with classical PI2, PI2-CMA, and PI2-KCCA, PI2-CMA-KCCA can not only endow the robot with the ability to realize transfer learning of trajectory planning from the demonstration to the new task, but also complete it more efficiently. The classical via-point experiments based on SCARA and Swayer robots have validated that the proposed method has fast learning convergence and can find a solution for the new task. Full article
(This article belongs to the Special Issue Biorobotics: Challenges, Technologies, and Trends)
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