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<p>Libraries, as centers of knowledge dissemination, require mobile robots to navigate dense bookshelves and dynamic pedestrian flows in their intelligent transformation. To address the unique challenges of library environments, this study develops an optimized DWA-RRT fusion algorithm for path planning. The method optimizes obstacle avoidance via node sampling, RRT global guidance, and force field principles to maintain safe distances. In multi-robot spatiotemporal trajectory planning, a spatiotemporal information fusion framework is constructed, combined with the gated recurrent unit-convolutional neural network to extract spatiotemporal features. Moreover, the graph attention network is improved to optimize communication. Experimental results showed that compared with the traditional dynamic window approach. The method increased average distances to static and dynamic obstacles to 0.61&#xa0;m and 1.85&#xa0;m, reduced path adjustment frequency, and smoothed linear speed fluctuations. The multi-robot collaborative task completion rate reached 89.76% to 98.62%, the average traffic time was shortened by 25&#xa0;s to 86&#xa0;s compared with the comparison method, and the number of collision risks was only 1/3–1/5 of the comparison method. It was suitable for different crowd density scenarios. This technology demonstrates potential to improve the robot’s adaptability and safety in the complex library environment through collaborative optimization of single-machine and multi-robot spatiotemporal trajectory planning. This suggests a promising approach that may balance safety and efficiency for robot autonomous navigation in library smart services and similar indoor service scenarios.</p>

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Mobile robot path planning for library smart services

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月8日

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  • Lijie Zhao

摘要

Libraries, as centers of knowledge dissemination, require mobile robots to navigate dense bookshelves and dynamic pedestrian flows in their intelligent transformation. To address the unique challenges of library environments, this study develops an optimized DWA-RRT fusion algorithm for path planning. The method optimizes obstacle avoidance via node sampling, RRT global guidance, and force field principles to maintain safe distances. In multi-robot spatiotemporal trajectory planning, a spatiotemporal information fusion framework is constructed, combined with the gated recurrent unit-convolutional neural network to extract spatiotemporal features. Moreover, the graph attention network is improved to optimize communication. Experimental results showed that compared with the traditional dynamic window approach. The method increased average distances to static and dynamic obstacles to 0.61 m and 1.85 m, reduced path adjustment frequency, and smoothed linear speed fluctuations. The multi-robot collaborative task completion rate reached 89.76% to 98.62%, the average traffic time was shortened by 25 s to 86 s compared with the comparison method, and the number of collision risks was only 1/3–1/5 of the comparison method. It was suitable for different crowd density scenarios. This technology demonstrates potential to improve the robot’s adaptability and safety in the complex library environment through collaborative optimization of single-machine and multi-robot spatiotemporal trajectory planning. This suggests a promising approach that may balance safety and efficiency for robot autonomous navigation in library smart services and similar indoor service scenarios.