This paper outlines a path planning method for autonomous rovers navigating urban environments without prior mapping, with a particular focus on addressing the Tsukuba Challenge. Our approach utilizes observations of pedestrian and robot movement trajectories to construct path graphs for global path planning. We provide a detailed overview of the autonomous rover’s hardware and software system, as well as a comprehensive description of the path planning algorithm. Our methodology entails extracting and continuously tracking dynamic objects from LiDAR data, resulting in the creation of a path graph based on their observed trajectories. Subsequently, a path aligned with the desired direction is selected. Notably, in indoor experimental settings, our approach proves effective, as the rover successfully generates a path to the goal by closely monitoring and tracking pedestrian movements. In conclusion, this paper introduces a promising path planning methodology and suggests potential areas for further research in autonomous mobility within uncharted environments.

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