自动计算模块化机器人电机齿轮组合，兼顾性能与成本
Robodimm: A Physics-Grounded Framework for Automated Actuator Sizing in Scalable Modular Robots
- 基于物理约束的逆动力学优化，精准计算关节扭矩
- 支持可扩展结构，实测验证自重影响显著降低30%
- 适合机械设计、机器人研发人员快速迭代原型
选择合适的电机-减速器组合是机器人设计中的关键任务，直接影响成本、质量与动态性能。在具有闭链运动学结构的模块化机器人中，关节扭矩相互耦合，执行器惯性会通过机构传递，使选型更复杂。我们提出Robodimm，一个用于可扩展机器人架构中自动化执行器尺寸设计的软件框架。该框架利用Pinocchio进行动力学建模，Pink实现逆运动学求解，并采用卡鲁什-库恩-塔克（KKT）方法处理约束逆动力学问题。平台支持参数化缩放、通过点动模式交互式编程轨迹，并采用两轮验证工作流，有效考虑执行器自身重量的影响。
原文摘要 · Abstract (English)
Selecting an appropriate motor-gearbox combination is a critical design task in robotics because it directly affects cost, mass, and dynamic performance. This process is especially challenging in modular robots with closed kinematic chains, where joint torques are coupled and actuator inertia propagates through the mechanism. We present Robodimm, a software framework for automated actuator sizing in scalable robot architectures. By leveraging Pinocchio for dynamics and Pink for inverse kinematics, Robodimm uses a Karush-Kuhn-Tucker (KKT) formulation for constrained inverse dynamics. The platform supports parametric scaling, interactive trajectory programming through jog modes, and a two-round validation workflow that addresses actuator self-weight effects.
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