This article presents a model predictive control based obstacle avoidance algorithm for autonomous ground vehicles in unstructured environments. The novelty of the algorithm is the simultaneous optimization of speed and steering without a priori knowledge about the obstacles. Obstacles are detected using a planar light detection and ranging sensor and a multi-phase optimal control problem is formulated to optimize the speed and steering commands within the detection range. Acceleration capability of the vehicle as a function of speed, and stability and handling concerns such as tire lift-off are taken into account as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Thus, a safe and quick navigation is enabled without the need for a preloaded map of the environment. Simulation results show that the proposed algorithm is capable of navigating the vehicle through obstacle fields that cannot be cleared with steering control alone.

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