Localization of Autonomous Robotic Vehicles Using A Neural-Network Approach

In this paper, a neural-network-based guidance methodology that utilizes line-of-sight based task-space sensory feedback is proposed for the localization of autonomous robotic vehicles. The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation). Herein, the proposed neural-network (NN) based guidance methodology is implemented on-line during the final stage of the vehicle's motion (i.e., docking). The systematic motion errors of the vehicle are reduced iteratively by executing the corrective motion commands, generated by the NN, until the vehicle achieves its desired pose within random noise limits. The guidance methodology developed was successfully tested via simulations for a 6-dof (degree-of-freedom) vehicle and via experiments for a 3-dof high-precision planar platform

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