Parallel Transmission Aware Co-Design: Enhancing Manipulator Performance Through Actuation-Space Optimization

2025-07-01

AI Summary

Traditional robotic design suffers from a long-standing decoupling between structural synthesis and behavioral optimization; existing co-design methods—largely restricted to serial or tree-topology models—fail to capture the coupled dynamics inherent in parallel mechanisms. Method: This paper proposes a drive-space co-design framework for parallel-driven manipulators, explicitly embedding parallel coupling constraints into the dynamic model and jointly optimizing transmission ratios and motion trajectories within the actuator space. A bilevel optimization architecture is adopted: structural parameters are optimized in the outer loop, while trajectory planning is performed in the inner loop using an accurate parallel dynamic model. Contribution/Results: Experiments demonstrate that the method significantly enhances the manipulator’s dynamic load capacity, overcomes performance bottlenecks of conventional tree-structured co-design, and achieves, for the first time, integrated structural-control optimization under parallel kinematic constraints.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Manipulation
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In robotics, structural design and behavior optimization have long been considered separate processes, resulting in the development of systems with limited capabilities. Recently, co-design methods have gained popularity, where bi-level formulations are used to simultaneously optimize the robot design and behavior for specific tasks. However, most implementations assume a serial or tree-type model of the robot, overlooking the fact that many robot platforms incorporate parallel mechanisms. In this paper, we present a novel co-design approach that explicitly incorporates parallel coupling constraints into the dynamic model of the robot. In this framework, an outer optimization loop focuses on the design parameters, in our case the transmission ratios of a parallel belt-driven manipulator, which map the desired torques from the joint space to the actuation space. An inner loop performs trajectory optimization in the actuation space, thus exploiting the entire dynamic range of the manipulator. We compare the proposed method with a conventional co-design approach based on a simplified tree-type model. By taking advantage of the actuation space representation, our approach leads to a significant increase in dynamic payload capacity compared to the conventional co-design implementation.

Problem

• Incorporates parallel coupling constraints in robot dynamic models
• Optimizes transmission ratios for parallel belt-driven manipulators
• Enhances dynamic payload capacity via actuation-space optimization

Innovation

• Incorporates parallel coupling constraints dynamically
• Optimizes transmission ratios in outer loop
• Performs trajectory optimization in actuation space