Forward Kinematics for Suspended Under-Actuated Cable-Driven Parallel Robots With Elastic Cables: A Neural Network Approach

Abstract

Kinematic analysis of under-constrained cable-driven parallel robots (CDPR) has been a topic of interest because of the inherent coupling between the loop-closure and 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 real-time forward geometrico-static analysis of such robots in a suspended configuration under the action of gravity. The formulation determines a non-linear function approximation to model the problem and proves to be efficient in solving consecutive and close waypoints in a path. The methodology is applied on a six-degree-of-freedom spatial under-constrained suspended CDPR. Specific comparison results in simulation and hardware to show the effectiveness of the proposed method in tracking a given path are illustrated. Finally, the degree of constraint satisfaction is presented against the results obtained from non-linear least-square optimization.