智能导航:用于自动驾驶汽车全局路径规划的谷歌 OR 工具和机器学习调查

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-08-11 DOI:10.1002/aisy.202300840
Alexandre Benoit, Pedram Asef
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引用次数: 0

摘要

我们对无人地面车辆(一种名为 ROMIE 的自主采矿采样机器人)的全局路径规划(GPP)进行了新的深入研究。全局路径规划对 ROMIE 的最佳性能至关重要,它可以转化为解决旅行推销员问题,这是一个复杂的图论难题,对于确定覆盖采矿场所有采样地点的最有效路线至关重要。这个问题对于通过优化成本和时间来提高 ROMIE 的运行效率和与人力相比的竞争力至关重要。本研究的主要目的是通过开发、评估和改进具有成本效益的软件和网络应用程序来推进 GPP。我们对谷歌运筹学(OR)工具的优化算法进行了广泛的比较和分析。我们的研究目标是通过首次集成强化学习技术来应用和测试 OR-Tools 功能的极限。这使我们能够将这些方法与 OR-Tools 进行比较,评估它们的计算效果和实际应用效率。我们的分析旨在深入了解每种技术的有效性和实际应用。我们的研究结果表明,Q-Learning 是最佳策略,在我们的数据集中平均仅偏离最佳解决方案 1.2%,表现出卓越的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Navigating Intelligence: A Survey of Google OR-Tools and Machine Learning for Global Path Planning in Autonomous Vehicles

We offer a new in-depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover all sampling locations in a mining field. This problem is central to enhancing ROMIE's operational efficiency and competitiveness against human labor by optimizing cost and time. The primary aim of this research is to advance GPP by developing, evaluating, and improving a cost-efficient software and web application. We delve into an extensive comparison and analysis of Google operations research (OR)-Tools optimization algorithms. Our study is driven by the goal of applying and testing the limits of OR-Tools capabilities by integrating Reinforcement Learning techniques for the first time. This enables us to compare these methods with OR-Tools, assessing their computational effectiveness and real-world application efficiency. Our analysis seeks to provide insights into the effectiveness and practical application of each technique. Our findings indicate that Q-Learning stands out as the optimal strategy, demonstrating superior efficiency by deviating only 1.2% on average from the optimal solutions across our datasets.

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CiteScore
1.30
自引率
0.00%
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审稿时长
4 weeks
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