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Abstract

Autonomous mobile robots have revolutionized navigation by operating without human intervention, leveraging advanced data acquisition systems such as cameras, radar, and LIDAR, along with sophisticated planning, localization, and control algorithms. A critical challenge in this domain is path planning: determining optimal trajectories to ensure safe and efficient travel in diverse environments. This paper addresses the need to systematically evaluate trajectory-planning algorithms, whose selection directly impacts navigation performance. We present a comprehensive analysis of various classes of path-planning methods, detailing their advantages, limitations, and application contexts. By comparing these algorithms, we identify key criteria for selecting the most suitable approach for a given scenario based on environmental complexity, computational constraints, and robot configuration. Our results synthesize existing solutions into a coherent framework that guides practitioners and researchers in algorithm choice. Finally, we discuss current challenges, such as real-time adaptability and scalability, and outline future prospects to advance mobile robot navigation systems. This work offers actionable insights to improve the reliability and performance of autonomous navigation.

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