This paper considers a multi-objective optimisation problem focusing on translational parallel manipulators. The proposed approach combines interval analysis with genetic algorithms to optimise the dynamic parameters of parallel manipulators. The objectives are to enhance the robot accuracy, while maximising the tolerance intervals of the parameters. This paper's contribution is to introduce an interval method to estimate the error bounds of a dynamic parallel manipulator within the desired workspace. A genetic algorithm is then applied to improve manipulator accuracy and reduce design costs. The resulting Pareto fronts illustrate the trade-off between each pair of objective functions.
