Robot Design Optimization

with Rotational and Prismatic Joints

using Black-Box Multi-Objective Optimization

IROS2024

  • Kento Kawaharazuka
  • Kei Okada
  • Masayuki Inaba
  • JSK Robotics Laboratory, The University of Tokyo, Japan

Robots generally have a structure that combines rotational joints and links in a serial fashion. On the other hand, various joint mechanisms are being utilized in practice, such as prismatic joints, closed links, and wire-driven systems. Previous research have focused on individual mechanisms, proposing methods to design robots capable of achieving given tasks by optimizing the length of links and the arrangement of the joints. In this study, we propose a method for the design optimization of robots that combine different types of joints, specifically rotational and prismatic joints. The objective is to automatically generate a robot that minimizes the number of joints and link lengths while accomplishing a desired task, by utilizing a black-box multi-objective optimization approach. This enables the simultaneous observation of a diverse range of body designs through the obtained Pareto solutions. Our findings confirm the emergence of practical and known combinations of rotational and prismatic joints, as well as the discovery of novel joint combinations.

Robot Design Optimization with Rotational and Prismatic Joints using Black-Box Multi-Objective Optimization

The overview of the proposed system. Joint modules are expressed using Xacro, and a robot URDF is generated by combining these modules. The generated URDF is then evaluated through simulation, and the subsequent designs are iteratively generated by black-box optimization.

The joint modules used in this study. The roll joint (R), the pitch joint (P), the yaw joint (Y), the two-axis orthogonal joint (O), the prismatic/sliding joint (S), and the fixed joint (F) are predefined. The design parameters encompass the type of the joint module and the length of the link L.

The random robot models generated through the combination of predefined joint modules.

Results for Target-ARM

Sampling results for Target-ARM. Each point represents a solution, with the Pareto solutions highlighted in red.

Several representative Pareto solutions (A) to (E) for Target-ARM. Each snapshot shows the result of inverse kinematics for the target position and orientation.

Results for Target-LEG

Sampling results for Target-LEG. Each point represents a solution, with the Pareto solutions highlighted in red.

Several representative Pareto solutions (A) to (F) for Target-LEG. Each snapshot shows the result of inverse kinematics for the target position and orientation.

Results for Target-WIDE

Sampling results for Target-WIDE. Each point represents a solution, with the Pareto solutions highlighted in red.

Several representative Pareto solutions (A) to (E) for Target-WIDE. Each snapshot shows the result of inverse kinematics for the target position and orientation.

Bibtex

@inproceedings{kawaharazuka2024slideopt,
  author={K. Kawaharazuka and K. Okada and M. Inaba},
  title={{Robot Design Optimization with Rotational and Prismatic Joints Using Black-Box Multi-Objective Optimization}},
  booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year=2024,
}

Contact

If you have any questions, please feel free to contact Kento Kawaharazuka.