This paper addresses the optimal synthesis problem of a cable driven parallel robot: LAWEX. A formulation of an optimization problem with 3 different objective functions for a specific task is proposed and then solved using the multi-objective genetic algorithm method. In more details, the LAWEX robot is optimised to perform a specific rehabilitation exercise of upper arm in terms of compactness, safety and energy comsuption.

This paper addresses the topological and dimensional synthesis of cable-driven parallel robots. A combined methodology for type and size optimization is proposed and applied to a cable driven parallel robot, which is intended for upper-limb rehabilitation exercises. The proposed approach deals with a set of deterministic and non-deterministic parameters within a multi-objective genetic algorithm. The proposed study aims to select a suitable architecture having optimal dimensions by considering several optimality criteria such as minimizing the cable tensions and achieving the smallest footprint. An illustrative application of the proposed synthesis approach is developed for LAWEX, a cable driven parallel robot for upper limb rehabilitation. Four different topology solutions have been considered for LAWEX robot. It is also proposed to use an additional safety criterion to select a solution on the obtained Pareto front.

This paper deals with the synthesis of a cable driven parallel robot intended for rehabilitation tasks. The objective is essentially to enhance the safety of patients using rehabilitation robotic devices. A novel safety criterion is proposed. It consists in maximizing the distance between the robot’s cables and the user. A dimensional synthesis of LAWEX robot considering the proposed safety criterion is carried out and optimal solutions are provided.

SUMMARY The optimum selection of a structure for a given application is a capital phase in typological synthesis of parallel robots. To help in this selection, this paper presents a performance evaluation of four translational parallel robots: Delta, 3-UPU, Romdhane-Affi-Fayet, and Tri-pyramid (TP). The problem is set as a multiobjective optimization using genetic algorithm methods, which uses kinematic criteria, that is, global dexterity and compactness, to ensure a prescribed workspace. The results are presented as Pareto fronts, which are used to compare the performances of the aforementioned structures. The obtained results show that the TP robot has the best kinematic performance, whereas the 3-UPU robot is the most compact for a given prescribed workspace.

In this paper, a comparative study is performed between a deterministic and a robust optimization methods, for the design of a translational parallel robot with a prescribed workspace. The optimization is defined in each case as a multi-objective formulation problem. The deterministic formulation is proposed to maximize the robot’s compactness as well as its dexterity. For the robust formulation, it is proposed to combine two methods, i.e., Genetic Algorithms and Monte Carlo methods, where the previous two objective functions were optimized along with their two standard deviations. A sensitivity analysis is then performed on each of the cases to evaluate the impact of the uncertainties on the design parameters on robustness of the obtained optimal solution. These two optimization methods are applied to optimize a 3-DOF parallel manipulator called Tri-pyramid.

The aim of this paper is to propose a geometric based approach for workspace analysis of Translational Parallel Manipulators (TPMs), which will be useful for the dimensional synthesis problem. For this purpose, a non-exhaustive list of TPMs in the bibliography is presented and grouped according to the structure of their legs as well as the shape of their workspaces. The approach is easy to implement and it is illustrated through three TMPs examples, having each a different workspace shape.