Project description
Precise robot control from imprecise but fast computations
Robot workers and autonomous vehicles rely on sensors to perceive their environment and software to process this information and decide what to do. In real-world situations, they need to compute commands as fast as possible. However, optimisation solvers in robots are designed to converge to a solution with high precision, which takes time. In this context, the Marie Skłodowska-Curie Actions (MSCA) project LP-NORM will investigate how low precision can be sufficient, and how this can be used to develop faster solvers. It will study what approximations are acceptable for the problem formulation and the solution. The project will focus on model predictive control and instantaneous linearised control, applied to a wide variety of systems, from buses to humanoid robots.
Objective
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.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences economics and business economics production economics productivity
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering robotics
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2021-PF-01
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78153 Le Chesnay Cedex
France
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