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In this paper, we design and validate a new tentacle-based approach, for avoiding obstacles during appearance-based navigation with a wheeled mobile robot. In the past, we have developed a framework for safe visual navigation. The robot follows a path represented as a set of key images, and during obstacle circumnavigation, the on-board camera is actuated to maintain scene visibility. In those works, the model used for obstacle avoidance was obtained using a potential vector field. Here, a more sophisticated and efficient method, that exploits the robot kinematic model, and predicts collision at look-ahead distances, is designed and integrated in that framework. Outdoor experiments comparing the two models show that the new approach presents many advantages. Higher speeds and precision can be attained, very cluttered scenarios involving large obstacles can be successfully dealt with, and the control inputs are smoother.

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