在风险环境中缩短路线长度的合作策略

Tecnura Pub Date : 2024-07-26 DOI:10.14483/22487638.19197
José Andrés Chaves Osorio, Jimy Alexander Cortés Osorio, Edward Andrés González Ríos
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引用次数: 0

摘要

目标:设计一种基于环境信息的人工智能系统,该系统可为个人、车辆或机器人推荐最短路径,使其在两点之间移动时感染冠状病毒 COVID-19 的风险最低:减少路径的合作策略包括一个管理和监控系统以及两个探索者代理。探索者代理配备了路径规划算法(GBFS 和 A*),并采用增量启发式方法,以找到两组不同的初步路径(第一组在起始目标方向,第二组在相反方向)。随后,管理和监控系统为每个路径规划器估算出一条初步最短路径,然后通过比较与路径规划器获得的路径,获得一条最短路径。这项研究出现在机器人分布式智能领域,目的是确定团队合作互动与个人工作相比的优势。在这项研究中,使用十种不同的环境执行了 300 次涉及合作策略的测试:本文的结果表明,在 79% 的分析情形中,通过合作策略获得的确定最短估计路径优于路径规划者单独找到的初步路径。超过 20.5% 的测试案例显著减少了路径(与最短确定路径相比超过 100%):在这项工作中,我们设计了一个人工智能系统,其测试表明该系统性能良好。该智能系统采用分布式智能技术,由一个管理和监控系统以及两个探索者代理组成的合作团队实施,他们根据环境信息,为想要在有冠状病毒 COVID-19 传染风险的环境中的两点之间旅行的个人、车辆或机器人推荐最短路径:本研究部分由佩雷拉技术大学(Universidad Tecnológica de Pereira)通过创新与扩展研究部(Vicerrectoría de Investigaciones Innovación y Extensión)支持,项目名称:Sistema de obtención de rutas más seguras bajo situación de pandemia caso covid-19,项目代码:3-20-11),部分由美国国家科学院(National Science Institute of Sciences)支持:3-20-11,部分由哥伦比亚国立大学提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estrategia cooperativa para reducir la longitud de la ruta en entornos riesgosos
Objective: Design an artificial intelligence system based on information from the environment that can recommend the shortest path to an individual or vehicle, or robot that moves between two points with the lowest risk of contagion with coronavirus COVID-19. Methodology: The cooperative strategy for path reduction involves a management and monitoring system and two explorer agents. Explorer agents are equipped with path planning algorithms (GBFS and A*) enhanced with incremental heuristics in order to find two different sets of preliminary paths (the first in direction start-goal and the second in the opposite direction). Subsequently, a management and monitoring system estimates a preliminary shortest path for each path planner then obtains a shortest path by comparing the paths attained with the path planners. This research emerges within the field of distributed intelligence in robotics to determine the benefits of teamwork interactions compared to individual work. In this study, 300 tests that involve the cooperative strategy were executed using ten different environments. Results: The results of this paper illustrate that in 79 % of analyzed situations, definitive shortest estimated paths obtained by cooperative strategy outperformed preliminary paths found individually by path planners. Over 20.5 % of tested cases yielded significant path reductions (greater than 100 % in relation to the shortest definitive path). Conclusions: In this work, an artificial intelligence system was designed, whose tests show a good performance. The intelligent system uses Distributed Intelligence implemented in a cooperative team formed by a management and monitoring system and two explorer agents, who, based on information from the environment, recommend the shortest path to an individual or vehicle or robot who wants to travel between two points located in an environment at risk of contagion with coronavirus COVID-19. Financing: This work was supported in part by the Universidad Tecnológica de Pereira through Vicerrectoría de Investigaciones Innovación y Extensión, Project name: Sistema de obtención de rutas más seguras bajo situación de pandemia caso covid-19, Project code: 3-20-11, and in part by the Universidad nacional de Colombia.
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