Two-Stage Optimization of a Reconfigurable Asymmetric 6-DOF Haptic Robot for Task-Specific Workspace

Abstract

Parallel mechanisms (PMs) are commonly used for developing haptic devices due to low inertia, high rigidity and precision. However, limited workspace impedes their application for task-oriented robotic therapy which generally requires large motion ranges. To solve this problem, first, a PM- based reconfigurable asymmetric 6-DOF haptic interface was presented, and then a two-stage optimization method was proposed to make the robot implement two kinds of task-specific workspaces including gross motor tasks (GMTs) and fine motor tasks (FMTs). Optimization of this robot was conducted to pursue a compact size and high accuracy. The global conditioning index (GCI) and the occupied area of the robot were selected as the evaluation indices, where the GCI was derived using a dimensionally homogeneous Jacobian matrix. A multi-objective optimization method based on the genetic algorithm (GA) was utilized. The actual design parameters were finally defined from solutions of the Pareto front. The proposed two-stage optimization method provides a feasible solution for determining task-specific robotic workspace of the reconfigurable mechanism.