Titlebook: Cable-Driven Parallel Robots; Proceedings of the 5 Marc Gouttefarde,Tobias Bruckmann,Andreas Pott Conference proceedings 2021 The Editor(s)

obtuse

表皮

The Learner and Digital Networksplicitly account for cable length measurement noise and are applicable to any unconstrained attitude parameterization of the CDPR’s end-effector. Nonlinear weighted least-squares optimization is used to solve the CDPR’s forward kinematics and determine covariance bounds on the pose estimation error

Maximize

The Learner and Digital Networksbecomes a non-linear kineto-static problem with the consideration of cable mass and elasticity. This problem is conventionally solved using numerical methods. These methods are iterative in nature, have slow convergence speed, and are highly dependent on initial values, which makes them difficult to

无所不知

Experience and Networked Learningd static equilibrium equations. The non-linearity of the problem is magnified with the addition of the coupling between the cable lengths and their tensions based on the elastic cable model. The paper proposes an unsupervised neural network algorithm to perform fast forward geometrico-static analysi

说不出

Experience and Networked Learningin the design phase as it will allow to choose the cable diameter and to provide a requested information for tuning the CDPR actuation. In this paper we consider a suspended N-1 CDPR with . cables where all cables are attached at the same point, which leads to a 3-dof robot. We assume a quasi-static

Acumen

粗鲁性质

Networked Learning: Perspectives and Issueserver. Cables can merely apply positive tensile force; thus, a CDPR’s control scheme should keep the cable tension positive. This is generally provided with a redundancy resolution (RR) scheme, which maps the control effort into a positive range without changing the motion characteristics of the rob

MAUVE

易改变

Highbrow