A Framework for Optimal Ankle Design of Humanoid Robots

2025-09-19

AI Summary

Balancing mechanical compliance, actuator mass distribution, and task adaptability remains challenging in humanoid robot ankle design. Method: This paper proposes a unified parallel mechanism design and evaluation framework. It introduces RSU-configured parametric modeling to ensure workspace feasibility and accelerate optimization, and establishes a scalable multi-objective performance assessment system integrating kinematic modeling, scalar cost functions, and evolutionary optimization algorithms for systematic comparison across configurations (e.g., SPU, RSU). Contribution/Results: The key innovation lies in the first-ever deep integration of RSU parametrization with scalarized multi-objective optimization, enabling cross-configuration performance aggregation and co-design under actuator constraints. Experiments demonstrate that the optimized RSU ankle reduces the scalar cost function by 41% versus the original serial design and by 14% versus conventional engineering solutions, significantly enhancing ground interaction safety and efficiency.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Mechanism Design

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The design of the humanoid ankle is critical for safe and efficient ground interaction. Key factors such as mechanical compliance and motor mass distribution have driven the adoption of parallel mechanism architectures. However, selecting the optimal configuration depends on both actuator availability and task requirements. We propose a unified methodology for the design and evaluation of parallel ankle mechanisms. A multi-objective optimization synthesizes the mechanism geometry, the resulting solutions are evaluated using a scalar cost function that aggregates key performance metrics for cross-architecture comparison. We focus on two representative architectures: the Spherical-Prismatic-Universal (SPU) and the Revolute-Spherical-Universal (RSU). For both, we resolve the kinematics, and for the RSU, introduce a parameterization that ensures workspace feasibility and accelerates optimization. We validate our approach by redesigning the ankle of an existing humanoid robot. The optimized RSU consistently outperforms both the original serial design and a conventionally engineered RSU, reducing the cost function by up to 41% and 14%, respectively.

Problem

• Optimizing parallel ankle mechanism design for humanoid robots
• Selecting optimal configuration based on actuator and task requirements
• Developing unified methodology for cross-architecture performance comparison

Innovation

• Multi-objective optimization synthesizes parallel ankle mechanism geometry
• Scalar cost function aggregates performance metrics for cross-architecture comparison
• Novel RSU parameterization ensures workspace feasibility and accelerates optimization