Abstract
This kineto-static problem of cable-driven parallel robot (CDPR) considering cable mass and elasticity is a highly non-linear and computationally complex problem. Numerical methods are conventionally employed to solve this problem. However, numerical methods are highly dependent on initial values, iterative in nature, computationally unbounded, and have slow convergence speed, making them unreliable for real-time scenarios. Therefore, this paper proposes a neural network-based approach to obtain the inverse kineto-static (IKS) as well as the forward kineto-static (FKS) solution of CDPR with significant cable mass and elasticity. The simulation results are presented for two cases, i.e., for a redundantly constrained planar CDPR (RP-CDPR) and a minimally constrained spatial CDPR (MS-CDPR). The training time, dataset requirement and testing error is observed to be significantly lower for MS-CDPR in comparison to RP-CDPR. For both the cases, the proposed approach shows a significant improvement in the computational speed when compared to numerical methods for both IKS and FKS problem. Owing to the advantage of bounded and low computational time, the proposed neural network-based approach is recommended for use in real-time applications.
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