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Motion planning under uncertainty is of significant importance forsafety-critical systems such as autonomous vehicles. Such systems have tosatisfy necessary constraints (e.g., collision avoidance) with potentialuncertainties coming from either disturbed system dynamics or noisy sensormeasurements. However, existing motion planning methods cannot efficiently findthe robust optimal solutions under general nonlinear and non-convex settings.In this paper, we formulate such problem as chance-constrained Gaussian beliefspace planning and propose the constrained iterative Linear Quadratic Gaussian(CILQG) algorithm as a real-time solution. In this algorithm, we iterativelycalculate a Gaussian approximation of the belief and transform thechance-constraints. We evaluate the effectiveness of our method in simulationsof autonomous driving planning tasks with static and dynamic obstacles. Resultsshow that CILQG can handle uncertainties more appropriately and has fastercomputation time than baseline methods.

Constrained Iterative LQG for Real-Time Chance-Constrained Gaussian Belief Space Planning