A digital design framework for the dimensional optimization of parallel robots based on kinematic and elasto-dynamic...

Area of Science:

• Robotics and Mechanical Engineering
• Computational Mechanics
• Optimization Techniques

Background:

• Dimensional optimization is crucial for parallel robot performance.
• Challenges include conflicting performance indices and high computational costs.
• Pose-dependent performance variations complicate optimal design.

Purpose of the Study:

• To present a framework for optimal parallel robot design.
• To integrate skeleton modeling, CAD-CAE, and multi-objective optimization.
• To address conflicts between kinematic and dynamic performance indices.

Main Methods:

• Developed kinematic and elasto-dynamic performance evaluation models using CAD/CAE.
• Employed Hard C-Means (HCM) clustering to identify representative poses.
• Utilized multi-objective optimization to find Pareto-optimal solutions for dimensional parameters.

Main Results:

• The proposed framework significantly enhances elasto-dynamic performance.
• Kinematic performance is maintained without excessive compromise.
• Computational time is significantly reduced compared to traditional methods.

Conclusions:

• The integrated framework effectively handles conflicting performance objectives.
• It provides a computationally efficient approach to parallel robot design.
• A developed software package facilitates accessible design of parallel/hybrid robots.