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Motion planning with an analytic risk cost for holonomic vehicles

We present a robust motion planning algorithm for a holonomic mobile robot that incorporates the risk of collisions directly in the cost function. This deterministic algorithm uses analytic predictions of the path-following error statistics to quickly calculate the collision risk. The A* search algorithm is used to find motion plans that optimally balance the plan duration with the collision risk, and these plans have a higher probability of success than plans that do not consider the collision risk. We present experimental results for an autonomous marine vehicle.