We address safe multi-robot interaction under uncertainty. In particular, we formulate a chance-constrained linear quadratic Gaussian game with coupling constraints and system uncertainties. We find a tractable reformulation of the game and propose a dual ascent algorithm. We prove that the algorithm converges to a generalized Nash equilibrium of the reformulated game, ensuring the satisfaction of the chance constraints. We test our method in driving simulations and real-world robot experiments. Our method ensures safety under uncertainty and generates less conservative trajectories than single-agent model predictive control.
View on arXiv@article{ren2025_2503.06776,
title={ Chance-constrained Linear Quadratic Gaussian Games for Multi-robot Interaction under Uncertainty },
author={ Kai Ren and Giulio Salizzoni and Mustafa Emre Gürsoy and Maryam Kamgarpour },
journal={arXiv preprint arXiv:2503.06776},
year={ 2025 }
}