Simulation Aided Co-Design for Robust Robot Optimization

This letter outlines a bi-level algorithm to concurrently optimize robot hardware and control parameters in order to minimize energy consumption during the execution of tasks and to ensure robust performance. The outer loop consists in a genetic algorithm that optimizes the co-design variables accor...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 7; no. 4; pp. 11306 - 11313
Main Authors: Fadini, Gabriele, Flayols, Thomas, Prete, Andrea Del, Soueres, Philippe
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Actuation
Co-design
Computational modeling
Computer Science
Design optimization
Dynamic programming
Energy consumption
Friction
Genetic algorithms
Hardware
Mathematics
Mechanism design
methods and tools for robot system design
Modeling and Simulation
Optimization
Optimization and Control
optimization and optimal control
Perturbation
Perturbation methods
Robot control
Robotics
Robots
robust/adaptive control
Robustness
Simulation
simulation and animation
Tracking control
Trajectory
Trajectory optimization
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:This letter outlines a bi-level algorithm to concurrently optimize robot hardware and control parameters in order to minimize energy consumption during the execution of tasks and to ensure robust performance. The outer loop consists in a genetic algorithm that optimizes the co-design variables according to the system average performance when tracking a locally optimal trajectory in perturbed simulations. The tracking controller exploits the locally optimal feedback gains computed in the inner loop with a Differential Dynamic Programming algorithm, which finds the optimal reference trajectories. Our simulations feature a complete actuation model, including friction compensation and bandwidth limits. This strategy can potentially account for arbitrary perturbations, and discards solutions that cannot robustly meet the task requirements. The results show improved performance of the designed platform in realistic application scenarios, autonomously leading to the selection of lightweight and more transparent hardware.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3200142