Year: 2025
Type: article
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.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412060334