Robot localization aided by quantum algorithms

2025-01-31

AI Summary

To address the computational bottleneck in real-time localization of mobile robots within large-scale maps using high-precision sensors (e.g., LiDAR), this work introduces Grover’s quantum search algorithm to 2D pose estimation for the first time, proposing a quantum-classical hybrid localization paradigm as an alternative to computationally intensive probabilistic methods such as AMCL. We design a robot-specific quantum encoding and measurement scheme and integrate high-resolution LiDAR data modeling within a ROS simulation environment. This yields a theoretical reduction in classical computational complexity. Experiments demonstrate a 62% average runtime reduction while maintaining equivalent localization accuracy. To our knowledge, this is the first scalable, simulation-validated prototype framework for quantum-enhanced robotic systems in the Noisy Intermediate-Scale Quantum (NISQ) era—bridging theoretical innovation with practical engineering feasibility.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Mobile Robotics
• Large-scale Mapping
• High-precision Sensor Data

Innovation

• Quantum Algorithms
• Grover's Search
• Robot Localization