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Robot localization aided by quantum algorithms
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DOI：10.1016/j.robot.2025.105026.png)
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Abstract
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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.
Keywords:
Quantum computing
Robot localization
Mobile robotics
Grover's algorithm
Navigation and mapping
Autonomous systems
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