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.

翻译：腿式机器人的性能强烈依赖于机械设计与控制，这促使采用协同设计方法对这些参数进行联合优化。然而，现有协同设计研究大多聚焦于连杆尺寸与传动比的优化，忽视了执行器的详细设计，特别是电机与减速器参数的优化，且主要局限于串联开链机构。本研究提出了一种面向平面闭链五杆单足机器人的协同设计框架，可联合优化机械设计、电机与减速器参数以及动态跳跃控制参数。目标是最大化跳跃距离的同时最小化机械能耗。该框架采用两阶段优化方法：首先通过执行器优化生成减速比到执行器质量、效率与峰值扭矩的映射关系，随后利用CMA-ES算法对机器人设计与控制进行协同优化。仿真结果表明，与标称设计相比，跳跃距离提升约42%，机械能耗降低15.8%，验证了所提框架在识别实现高性能、高能效平面跳跃的最优设计参数、执行器参数及控制参数方面的有效性。