Fault-tolerant multi-robot localization: diagnostic decision-making with information theory and learning models

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Autonomous Robots Pub Date : 2025-04-17 DOI:10.1007/s10514-025-10196-6

Zaynab El Mawas, Cindy Cappelle, Maan El Badaoui El Najjar

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Abstract

In the domain of multi-robot systems, cooperative systems that are highly attuned and connected to their surroundings are becoming increasingly significant. This surge in interest highlights various challenges, especially regarding system integration and safety constraints. Our research contributes to the assurance of fault tolerance to avert abnormal behaviors and sustain reliable robot localization. In this paper, a mixed approach between data-driven and model-based for fault detection is introduced, within a decentralized architecture, thereby strengthening the system’s capacity to handle simultaneous sensor faults. Information theory-based fault indicators are developed by computing the Jensen-Shannon divergence (\(D_{JS}\)) between state predictions and sensor-obtained corrections. This initiates a two-tiered data-driven mechanism: one layer employing Machine Learning for fault detection, and another distinct layer for fault isolation. The methodology’s efficacy is assessed using real data from the Turtlebot3 platform.

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多机器人容错定位：利用信息论和学习模型进行诊断决策

在多机器人系统领域，与周围环境高度协调和连接的协作系统变得越来越重要。这种兴趣的激增突出了各种挑战，特别是关于系统集成和安全约束。我们的研究有助于保证容错，避免异常行为和维持可靠的机器人定位。本文提出了一种基于数据驱动和基于模型的混合故障检测方法，该方法采用分散式结构，从而增强了系统处理传感器同步故障的能力。基于信息理论的故障指示器是通过计算状态预测和传感器获得的修正之间的Jensen-Shannon散度（\(D_{JS}\)）来开发的。这启动了一个两层数据驱动机制：一层使用机器学习进行故障检测，另一层用于故障隔离。使用来自Turtlebot3平台的真实数据评估该方法的有效性。

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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来源期刊

Autonomous Robots 工程技术-机器人学

CiteScore

7.90

自引率

5.70%

发文量

审稿时长

3 months

期刊介绍： Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development. The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.