HEИндивидуална стипендия2022–2024

LP-NORM · Leveraging Precision in Numerical Optimization for Robotic Motions

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2022-08-01 → 2024-07-31
Финансиране от ЕС: 211 755 €
Участници: 1
Схема: HORIZON-TMA-MSCA-PF-EF

Линиите свързват координатора с партньорите.

Накратко на български

Алгоритмите за движение на роботи, като тези при автономните автомобили, се изследват чрез числена оптимизация. Целта е да се разбере дали по-малко прецизните изчисления могат да осигурят същата ефективност, тъй като сензорите и моделите на реалността са несъвършени.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Leveraging Precision in Numerical Optimization for Robotic Motions

Robotic systems are expected to take a large place in tomorrow’s society, from self-driving vehicles to humanoid robots, far beyond current industrial robots in tightly controlled factory environments. Disruptive domains include autonomous cars or buses for transportation, robotic arms in collaboration with workers, quadruped robots for inspection or as companion workers, humanoid robots to help fragile people or to relieve operators from tedious, MSD-inducing or low-added value tasks… and even landing-capable rockets. These widely different robotic systems all share a common approach when it comes to algorithms controlling their motion: these motions are designed by specifying numerical objectives and constraints on what these robotic systems must do, and within which limits. These specifications often conflict, and actual motor controls must then be computed to satisfy all these objectives and constraints in the best possible way. This is naturally achieved by solving a numerical optimization problem. Optimization-base control is a very effective and popular solution. The problem arising in robotics are small enough that they can be solved exactly in theory and to the extent permitted by the computer precision in practice. Yet these problems (whether solved online or offline – e.g. in learning-based approach) relies on models, which imperfectly reflects the reality. The control is also based on inputs from sensors with a limited precision and the robot actuators can not exactly follow the command computed. So, do we really need exact, or even precise, numerical solutions? The goal of the project was to explore two hypotheses: - (H1) We can obtain the exact same performance with imprecise numerical solutions - (H2) We can obtain these imprecise numerical solutions using less costly numerical methods Three objectives were defined to explore them: - (O1) Provide a diverse benchmark for optimization-based control of robotic systems - (O2) Provide a detailed impact analysis of numerical precision in models and solutions - (O3) Provide an efficient solver tailored for inexact computations Additionally, the project looks at the environmental impacts of robotics, in particular to see how the findings can accompanied to reduce the overall computational footprint of robots rather than help more robots being built as a result of a cheaper operational cost.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Automated vehicles and complex robot workers are expected to be used massively soon, with positive impacts on security, health at work and productivity. To handle real-world situations, they need to compute their command as fast as possible, but the advanced, safe control algorithms remain a computational bottleneck. To find the solution to a set of motion specifications and constraints for a robot, a widely used approach is to formulate and solve an optimization problem. The formulation is necessarily imprecise, due to modeling, sensing and estimation errors and the solution will not be executed perfectly by the robot. Yet the optimization solvers used in robotics are designed to converge to an exact solution with high precision, wasting time.In this project, I make a change of paradigm by leveraging approximations and investigate how the absence of need for high precision can be used to develop faster solvers. I study what approximations or errors are acceptable for the problem formulation and the solution, paying attention to the numeric properties of the problem. I use this knowledge to develop a solver tailored for approximate computations, with an emphasize on cheap but imprecise inner iterations and early termination. It will also handle gracefully infeasible situations due to errors, making it safer to operate in real conditions.To make the study, and test and benchmark the solver, I focus on two families of control problems: model predictive control and instantaneous linearized control, applied to a wide variety of systems, from buses, to rockets, to humanoid robots.This solver will have important impacts: make it possible to achieve real-time control for the most complex system; allow to keep real-time, when it was already possible, while enriching the problems; reduce the computing power and energy consumption required for a given robot. Understanding and handling imprecisions would also allow to build less precise and thus cheaper robots.

Оригинален текст от CORDIS (на английски).

Участници

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE · Le Chesnay CedexКоординаторФранция