Modeling and Multi-objective Optimization of a Kind of Teaching Manipulator
This work addresses the problem of efficiently designing teaching manipulators for industrial robots, but it is incremental as it applies existing optimization methods to a specific device.
The paper tackles the design optimization of a six degree-of-freedom teaching manipulator without actuators, focusing on improving gravity balance and operating force performance, and reports that the optimized solutions outperform a human expert's design.
A new kind of six degree-of-freedom teaching manipulator without actuators is designed, for recording and conveniently setting a trajectory of an industrial robot. The device requires good gravity balance and operating force performance to ensure being controlled easily and fluently. In this paper, we propose a process for modeling the manipulator and then the model is used to formulate a multi-objective optimization problem to optimize the design of the testing manipulator. Three objectives, including total mass of the device, gravity balancing and operating force performance are analyzed and defined. A popular non-dominated sorting genetic algorithm (NSGA-II-CDP) is used to solve the optimization problem. The obtained solutions all outperform the design of a human expert. To extract design knowledge, an innovization study is performed to establish meaningful implicit relationship between the objective space and the decision space, which can be reused by the designer in future design process.