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Manipulator system selection based on evaluation of task completion time and cost

Task completion time and cost are two significant criteria for the selection of manipulator system. For a given task, several Pareto solutions of manipulator systems should be derived based on the evaluation of these two criteria. However, this process requires a large calculation time. In this paper, we propose a method that can select appropriate systems by evaluating task completion time and cost within the desired calculation time. In the proposed method, multiple objective particle swarm optimization (MOPSO) is employed to search for appropriate manipulator systems from a set of candidate systems. Location optimization and motion coordination are integrated to derive the task completion time and the relative cost is used to evaluate the cost of a manipulator system. We employ particle swarm optimization (PSO) for location optimization and use nearest-neighborhood algorithm (NNA) for motion coordination, since PSO and NNA have a high speed of convergence to a good solution. The proposed method is applied to a set of tasks and is proved to be effective and practical.