Configuration Optimization of an Auto-reconfigurable Cable-Driven Upper-Limb Rehabilitation Robot

Compared with traditional rigid structures, cable-driven parallel robots (CDPRs) have been widely used in rehabilitation training due to the lower motion inertia and cable flexibility. However, for the traditional CDPRs, configurations cannot be changed individually according to different patients and different scenes, thus the adaptability is limited and it is difficult to provide satisfactory training effects. In this paper, an auto-reconfigurable cable-driven upper-limb rehabilitation robot (ACURR) is put forward, which can change the configurations according to different rehabilitation training requirements automatically. Based on the traditional wrench feasible workspace, the performance space of the ACURR is defined according to the further analysis of cable tensions. When the ACURR moves in the performance space, patients can bear more comfortable tensions and the training system is more secure. An optimization problem is then formulated to obtain the optimal configurations of the ACURR by maximizing the performance space, and an optimization algorithm is proposed to solve it by combining the interval analysis method and the simulated annealing algorithm. The correctness and effectiveness of the algorithm are verified with the simulations, which also shows that the concept of the ACURR is reasonable and the optimal configurations can always be obtained in different scenarios.