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From CAD to URDF: Co-Design of a Jet-Powered Humanoid Robot Including CAD Geometry

Punith Reddy Vanteddu, Gabriele Nava, Fabio Bergonti, Giuseppe L'Erario, Antonello Paolino, Daniele Pucci

TL;DR

This paper identifies the robot links that significantly influence control performance and mechanical design of the jet-powered humanoid robot iRonCub and introduces a co-design framework aimed at improving both the control performance and mechanical design of the robot.

Abstract

Co-design optimization strategies usually rely on simplified robot models extracted from CAD. While these models are useful for optimizing geometrical and inertial parameters for robot control, they might overlook important details essential for prototyping the optimized mechanical design. For instance, they may not account for mechanical stresses exerted on the optimized geometries and the complexity of assembly-level design. In this paper, we introduce a co-design framework aimed at improving both the control performance and mechanical design of our robot. Specifically, we identify the robot links that significantly influence control performance. The geometric characteristics of these links are parameterized and optimized using a multi-objective evolutionary algorithm to achieve optimal control performance. Additionally, an automated Finite Element Method (FEM) analysis is integrated into the framework to filter solutions not satisfying the required structural safety margin. We validate the framework by applying it to enhance the mechanical design for flight performance of the jet-powered humanoid robot iRonCub.

From CAD to URDF: Co-Design of a Jet-Powered Humanoid Robot Including CAD Geometry

TL;DR

This paper identifies the robot links that significantly influence control performance and mechanical design of the jet-powered humanoid robot iRonCub and introduces a co-design framework aimed at improving both the control performance and mechanical design of the robot.

Abstract

Co-design optimization strategies usually rely on simplified robot models extracted from CAD. While these models are useful for optimizing geometrical and inertial parameters for robot control, they might overlook important details essential for prototyping the optimized mechanical design. For instance, they may not account for mechanical stresses exerted on the optimized geometries and the complexity of assembly-level design. In this paper, we introduce a co-design framework aimed at improving both the control performance and mechanical design of our robot. Specifically, we identify the robot links that significantly influence control performance. The geometric characteristics of these links are parameterized and optimized using a multi-objective evolutionary algorithm to achieve optimal control performance. Additionally, an automated Finite Element Method (FEM) analysis is integrated into the framework to filter solutions not satisfying the required structural safety margin. We validate the framework by applying it to enhance the mechanical design for flight performance of the jet-powered humanoid robot iRonCub.

Paper Structure

This paper contains 17 sections, 10 equations, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Notation
  4. Robot Modelling
  5. Momentum-based Flight Controller
  6. Co-Design Framework
  7. CAD Geometry
  8. FEM Structural Analysis
  9. URDF Generation
  10. Flight Simulation
  11. Design Optimization with NSGA-II
  12. Results
  13. Framework Specifications
  14. Co-Design Output
  15. Validation
  16. ...and 2 more sections

Figures (8)

  • Figure 1: iRonCub-Mk3 CAD model
  • Figure 2: Representation of the co-design framework and the individual evaluation strategy. The brown arrows represent the constraints that must be satisfied to obtain a feasible solution.
  • Figure 3: Geometry of the components to be optimized for enhancing jet positions and orientations. The geometry parameters Angle, Distance, Length, and Offset are collected in a vector $\theta$.
  • Figure 4: FEM analysis for stress distribution in the generated CAD components.
  • Figure 5: Snapshots of the flight simulator and the flight envelope used for design optimization.
  • ...and 3 more figures