Chance-Constrained Linear Quadratic Gaussian Games for Multi-Robot Interaction Under Uncertainty

Kai Ren, Giulio Salizzoni, Mustafa Emre Gürsoy, Maryam Kamgarpour

Abstract: 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 feedback 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.

External IDs:doi:10.1109/lcsys.2025.3588090