Stellantis is a multinational automotive manufacturing corporation formed in 2021 on the basis of a 50-50 cross-border merger between the Italian-American conglomerate Fiat Chrysler Automobiles and the French PSA Group. The principal activity of Stellantis is the design, development, manufacture and sale of automobiles.

The objective of this project is to work on the required principles to avoid the classic restrained static security zones, synthesizing a dynamic representation of the shared workspace to take advantage of state of the art control laws, allowing a fluid collaboration between human-robot. This dynamic synthesis requires knowledge of the robots state (geometric, cinematic and cognitive, such as fatigue, expertise and situation awareness), its tasks, capacities, state of humans that surround it and their tasks. Furthermore,it needs to achieve a formal and provable online algorithm that correctly estimates the state of the human and guarantee a safe shared workspace tackling ambitious scientific questions poorly addressed in literature.

People involved:

• David Daney
• Vincent Padois
• Nicolas Torres

Related topics #stellantis

Model Predictive Control for robots adapting their task space motion online

High-frequency trajectory re-planning via MPC

 Posted on April 19, 2023  |  Auctus

Paper Abstract Robots require the ability to autonomously and continuously react to unexpected online changes in the task definition and in the environment, especially those cohabited with humans. To react to these changes, the task, from the current state up to the finish, must instantly be reconsidered. This implies a prohibitive re-computation cost. This paper proposes a modular control architecture based on Model Predictive Control, that offers a good compromise between optimally achieving … [Read More]

Linear Model Predictive Control in SE(3) for online trajectory planning in dynamic workspaces

A linear MPC in position and orientation for safe trajectory planning

 Posted on September 29, 2022  |  Auctus

Paper Abstract Efficient workspace sharing of collaborative robots and human operators remains an unsolved problem in the industry. This problem goes beyond the use of a priori or a posteriori safety measures and has to be tackled at the control level. To address the need of adaptation to human presence as well as to endow the robot with the ability to adapt interactively to new Cartesian targets, a linear Model Predictive Controller is proposed in this paper. This controller computes … [Read More]

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