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A novel line-of-sight sensing-based modelless guidance strategy is presented for the autonomous docking of robotic vehicles. The novelty of the proposed guidance strategy is twofold: 1) applicability to situations that do not allow for direct proximity measurement of the vehicle and 2) ability to generate short-range docking motion commands without a need for a global sensing-system (calibration) model. Two guidance -based motion-planning methods were developed to provide the vehicle controller with online corrective motion commands: a passive-sensing-based and an active-sensing-based scheme, respectively. The objective of both proposed guidance methods is to minimize the accumulated systematic errors of the vehicle as a result of the long-range travel, while allowing it to converge to its desired pose within random-noise limits. Both techniques were successfully tested via simulations and experiments, and are discussed herein, in terms of convergence rate and accuracy, in addition to the types of localization problems for which each method could be specifically more suitable.