• CN: 11-2187/TH
  • ISSN: 0577-6686

›› 2012, Vol. 48 ›› Issue (17): 10-20.

• Article • Previous Articles     Next Articles

Mechanism-parameters Design Method of an Amphibious Transformable Robot Based on Multi-objective Genetic Algorithm

LI Nan;WANG Minghui;MA Shugen;LI Bin;WANG Yuechao   

  1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences Graduate School, Chinese Academy of Sciences Department of Technology, Ritsumeikan University
  • Published:2012-09-05

Abstract: As a new style of the mobile robot, the amphibious transformable robot can not only perform reconfiguration but also implement tasks in amphibious environment. For the mechanism design of the robot, the parameter of the mechanism takes influence on the robot’s performance in the task environment. To implement the performance optimization in the complex environment and task, a mechanism-parameters design method of an amphibious transformable robot based on multi-objective genetic algorithm is proposed. Based on the kinematics and dynamic analysis of the robot, the multi-objective optimization problem of the mechanism parameters design is established on the mapping relationships between the performance indexes and the mechanism parameters. The non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve this optimization problem and get the Pareto optimization. A solution of optimization problem of the mechanism parameters is extracted through the Pareto optimization based on the optimizing method of combination weighting of multi-attribute decision-making, and then the result is used to direct the mechanism design of the amphibious transformable robot, Amoeba-II. The experiment for the maneuverability of Amoeba-II in the amphibious environment is performed to certify the validity and applicability of the mechanism-parameters design method of amphibious transformable robot based on multi-objective genetic algorithm.

Key words: Amphibious transformable robot, Mechanism-parameters, Multi-objective genetic algorithm, Pareto optimization

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