Analysis of Various Manipulator Configurations Based on Multi-Objective Black-Box Optimization

TL;DR

The paper tackles the challenge of understanding optimal manipulator structure by jointly optimizing discrete joint configurations and continuous link lengths for 6-DOF and 7-DOF arms. It introduces a design pipeline that parameterizes joints, automatically generates URDFs via xacro, and evaluates end-effector reachability and joint torque through a voxelized workspace and IK, formalized as $E^{reach}$ and $E^{torque}$. A multivariate Tree-Structured Parzen Estimator (TPE) search identifies Pareto-optimal and novel configurations (e.g., PRRY, YPRR) and maps existing robots within the generated design space. The findings highlight how joint distribution and gravity influence reachability and torque, offering guidance for future manipulator designs while acknowledging limitations like manufacturability and the exclusion of more complex joint types.

Abstract

Various 6-degree-of-freedom (DOF) and 7-DOF manipulators have been developed to date. Over a long history, their joint configurations and link length ratios have been determined empirically. In recent years, the development of robotic foundation models has become increasingly active, leading to the continuous proposal of various manipulators to support these models. However, none of these manipulators share exactly the same structure, as the order of joints and the ratio of link lengths differ among robots. Therefore, in order to discuss the optimal structure of a manipulator, we performed multi-objective optimization from the perspectives of end-effector reachability and joint torque. We analyze where existing manipulator structures stand within the sampling results of the optimization and provide insights for future manipulator design.

Figures (6)

• Figure 1: Research concept: By performing multi-objective optimization from the perspectives of end-effector reachability and joint torque, this study discusses various joint structures, including the diverse manipulators that have been actively developed in recent years.
• Figure 2: System overview: This study parametrizes joint types and link lengths, automatically generates URDFs, and performs multi-objective optimization from the perspectives of end-effector reachability and joint torque to discuss the structures of various manipulators.
• Figure 3: Design parameters handled in this study: The joint type $J_i$ and link length $L_i$ are varied as design parameters.
• Figure 4: Objective functions for multi-objective optimization: The left image shows end-effector reachability, while the right image shows necessary joint torque.
• Figure 5: Optimization results for a 6-DOF manipulator: The upper figure visualizes the sampling results, color-coded based on differences in the first four joint types. The lower figure presents a subset of the solutions, showing end-effector reachability, joint torque, and four postures generated by assigning random joint angles.