Mobile Multi-Robot Navigation under Runtime Uncertainty via Koopman Operator Learning and Nonlinear Model Predictive Control

2026-09-12

AI Summary

本文通过Koopman算子学习和非线性模型预测控制解决移动多机器人在运行时不确定性下的导航问题，实现了目标到达与编队控制。

Abstract

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 Koopman operator-based model used in the NMPC formulation addresses runtime uncertainties, namely, various degrees of random wheel slipping. We validated the effectiveness of our method for both problems via extensive numerical simulations in different environments with wheeled robots affected by different amounts of slip and without knowledge of their true dynamic models.

Problem

• Multi-Robot Navigation
• Runtime Uncertainty
• Koopman Operator
• Nonlinear Model Predictive Control
• Formation Control

Innovation

• Koopman Operator
• Nonlinear Model Predictive Control
• Runtime Uncertainty