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.

Simulation Aided Co-Design for Robust Robot Optimization / Fadini, G; Flayols, T; Del Prete, A; Soueres, P. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 7:4(2022), pp. 11306-11313. [10.1109/LRA.2022.3200142]

Simulation Aided Co-Design for Robust Robot Optimization

Del Prete, A;
2022-01-01

Abstract

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.
2022
4
Fadini, G; Flayols, T; Del Prete, A; Soueres, P
Simulation Aided Co-Design for Robust Robot Optimization / Fadini, G; Flayols, T; Del Prete, A; Soueres, P. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 7:4(2022), pp. 11306-11313. [10.1109/LRA.2022.3200142]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/376871
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