Towards Autonomy in Unmanned Vehicles Using Receding Horizon Strategies

Marina Murillo, Guido Sánchez, Lucas Genzelis, Nestor N. Deniz, Leonardo Giovanini


In this article we propose to use receding horizon strategies, like model predictive control (MPC) and moving horizon estimation (MHE), to design guidance, navigation and path-planning tasks, which play an essential role in autonomy of unmanned vehicles. As we propose to design these tasks using MPC and MHE, the physical and dynamical constraints can be included at the design stage, thus leading to optimal and feasible results. In order to evaluate the performance of the proposed framework, we have used Gazebo simulator in order to drive a Jackal unmanned ground vehicle (UGV) along a desired path computed by the path-planning module. The results we have obtained are successful as the estimation and guidance errors are small and the Jackal UGV is able to follow the desired path satisfactorily and it is also capable to avoid the obstacles which are in its way.

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