Abstract
Well-known environments allow for the creation of open-loop robotic controllers (controllers that do not rely on sensor feedback). Unexpected obstacles in the robot path would render an open-loop controller useless and would require a sophisticated and complex closed-loop controller. This problem is addressed by the developed approach that uses command sampling and neural network based localization to temporarily take control and safely navigate around unexpected obstacles, when detected. Control is then relinquished back to the base controller to perform the original task. Experiments performed on a real robot highlight the viability of the approach for short-term navigation, but adjustments are required for longer paths.
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Botha, B., du Plessis, M.C. (2022). Avoiding Unexpected Obstacles During Robotic Navigation Using Rapidly-Exploring Random Trees and a Neural Network Simulator. In: Jembere, E., Gerber, A.J., Viriri, S., Pillay, A. (eds) Artificial Intelligence Research. SACAIR 2021. Communications in Computer and Information Science, vol 1551. Springer, Cham. https://doi.org/10.1007/978-3-030-95070-5_15
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DOI: https://doi.org/10.1007/978-3-030-95070-5_15
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