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Chance-constrained Linear Quadratic Gaussian Games for Multi-robot Interaction under Uncertainty
Kai Ren, Giulio Salizzoni, Mustafa Emre Gürsoy, Maryam Kamgarpour
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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.
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