用量子搜索算法加速机器人定位，提升效率。
Robot localization aided by quantum algorithms
- 将格罗弗算法应用于二维地图，优化定位搜索过程。
- 实验显示相比经典方法有显著提速，验证量子优势。
- 适合对高效定位有需求的机器人研究者参考。
定位是移动机器人导航中的关键环节，使机器人能高效运行并避障。现有概率定位方法如自适应蒙特卡洛定位（AMCL）计算开销大，面对大地图或高分辨率传感器数据时表现受限。本文探索量子计算在机器人领域的应用，聚焦于使用格罗弗搜索算法提升移动机器人定位效率。提出一种在二维地图中利用格罗弗算法的新方法，实现更快更高效的定位。尽管当前物理量子计算机存在局限，实验结果仍表明其相较于经典方法具有显著速度优势，凸显了量子计算在提升机器人定位方面的潜力。本工作弥合了量子计算与机器人学之间的鸿沟，为机器人定位提供了一种可行方案，并为未来量子机器人研究铺平道路。
原文摘要 · Abstract (English)
Localization is a critical aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Current probabilistic localization methods, such as the Adaptive-Monte Carlo localization (AMCL) algorithm, are computationally intensive and may struggle with large maps or high-resolution sensor data. This paper explores the application of quantum computing in robotics, focusing on the use of Grover's search algorithm to improve the efficiency of localization in mobile robots. We propose a novel approach to utilize Grover's algorithm in a 2D map, enabling faster and more efficient localization. Despite the limitations of current physical quantum computers, our experimental results demonstrate a significant speedup over classical methods, highlighting the potential of quantum computing to improve robotic localization. This work bridges the gap between quantum computing and robotics, providing a practical solution for robotic localization and paving the way for future research in quantum robotics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。