SOURCE-LINKED INTELLIGENCE
Mobile Multi-Robot Navigation under Runtime Uncertainty via Koopman Operator Learning and Nonlinear Model Predictive Control
In this work, we developed a nonlinear model predictive control (NMPC) framework that employs learned dynamics via the Koopman Operator theory for mobile multi-robot navigation. We formulated and solved NMPC problems using a lifted bilinear Koopman-based model that accurately predicts affine input systems affected by perturbations and uncertainties. Two exemplary multi-robot navigation problems are considered: target reaching and formation control. The output of our method enables closed-loop multi-robot navigation and formation control in environments populated with obstacles, whereby the Koo
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T17:16:11.000Z
First collected: 2026-09-20T12:41:04.663Z. This is not the publication date.