A Co-Design Framework for High-Performance Jumping of a Five-Bar Monoped with Actuator Optimization

2026-04-07

AI Summary

This work addresses the limitations of existing co-design approaches for legged robots, which often neglect actuator parameter optimization and are confined to open-chain architectures, thereby hindering high-performance jumping. The paper proposes a co-design framework tailored for a planar closed-chain five-bar monopedal robot, uniquely incorporating detailed actuator specifications—such as motor and gearbox mass, efficiency, and peak torque—into whole-system optimization. The framework jointly optimizes mechanical structure, actuator selection, and control policy through a two-stage methodology: first establishing a mapping between gear ratios and actuator performance, then applying the CMA-ES algorithm for global co-optimization. Simulation results demonstrate that the optimized design achieves a 42% increase in jump distance and a 15.8% reduction in mechanical energy consumption compared to a baseline configuration.

Abstract

The performance of legged robots depends strongly on both mechanical design and control, motivating co-design approaches that jointly optimize these parameters. However, most existing co-design studies focus on optimizing link dimensions and transmission ratios while neglecting detailed actuator design, particularly motor and gearbox parameter optimization, and are largely limited to serial open-chain mechanisms. In this work, we present a co-design framework for a planar closed-chain five-bar monoped that jointly optimizes mechanical design, motor and gearbox parameters, and control parameters for dynamic jumping. The objective is to maximize jump distance while minimizing mechanical energy consumption. The framework uses a two-stage optimization approach, where actuator optimization generates a mapping from gear ratio to actuator mass, efficiency, and peak torque, which is then used in co-design optimization of the robot design and control using CMA-ES. Simulation results show an improvement of approximately 42% in jump distance and a 15.8% reduction in mechanical energy consumption compared to a nominal design, demonstrating the effectiveness of the proposed framework in identifying optimal design, actuator, and control parameters for high-performance and energy-efficient planar jumping.

Problem

• co-design
• actuator optimization
• legged robots
• closed-chain mechanism
• dynamic jumping

Innovation

• co-design
• actuator optimization
• five-bar monoped
• dynamic jumping
• CMA-ES