obtuse
发表于 2025-3-28 15:25:44
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表皮
发表于 2025-3-28 20:09:28
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
发表于 2025-3-28 22:54:14
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
发表于 2025-3-29 04:44:32
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
发表于 2025-3-29 10:51:16
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
发表于 2025-3-29 15:07:02
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粗鲁性质
发表于 2025-3-29 19:35:46
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
发表于 2025-3-29 22:59:34
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易改变
发表于 2025-3-30 01:01:49
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Highbrow
发表于 2025-3-30 06:40:39
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