Motion planning of a redundant manipulator-modeling of criteria of skilled operators and global optimization of redundancy

This paper proposes a modeling method of criteria of skilled operators for motion planning of a redundant manipulator in industrial applications. The method employs fuzzy-ID3 to extract important factors with certainties from the criteria and GMDH (group method of data handling) to model it to evaluate motion plans. Then, it applies a genetic algorithm to optimize redundancy of a manipulator. The proposed method reduces the operator's labor and time for task teaching process thus a path, without considering redundant parameters, only needs to be determined. Experimental results show the effectiveness of the proposed method.

Authors

• Takanori Shibata
• Tamotsu Abe
• Kazuo Tanie
• Matsuo Nose

Keywords

• Optimization methods
• Manipulator dynamics
• Redundancy
• Education
• Service robots
• Educational robots
• Data mining
• Data handling
• Genetic algorithms
• Motion planning
• Global Optimization
• Path Planning
• Operational Skills
• Teaching Process
• Degrees Of Freedom
• Training Data
• Maximum And Minimum
• Evaluation Of Function
• Decision Tree
• Numerical Data
• Angular Velocity
• Rotation Angle
• Information Gain
• Membership Function
• Fuzzy Set
• Leaf Node
• Piece Of Work
• Crossover Operator