A Path Correction Module for two-wheeled service robots under actuator faults

Stavrou, Demetris; Ηλιάδης, Δημήτριος Γ.; Panayiotou, Christos G.

doi:10.1109/med.2013.6608861

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…Actuators play a crucial role in enabling service robots to interact with their environment. The imperative for reliable operation naturally led to an increased focus on the maintenance and performance of these components [67,68]. Additionally, given the diverse range of potential actuator failures, encompassing sensor failures, motion system malfunctions, control system issues, and other failure modes, it becomes essential to include comprehensive diagnostic and monitoring techniques tailored explicitly for service robot actuators.…”

Section: Topic Popularity Analysismentioning

confidence: 99%

Structural topic modeling-based systematic review for service robots’ fault diagnosis: content, popularity and regional differences

Wang,

Dong,

Qian

et al. 2024

Meas. Sci. Technol.

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The market for service robots is expanding as labor costs continue to rise. Faced with intricate working environments, fault detection and diagnosis are crucial to ensure the proper functioning of service robots. The objective of this review is to systematically investigate the realm of service robots’ fault diagnosis through the application of Structural Topic Modeling (STM). A total of 289 papers were included, culminating in ten topics, including advanced algorithm application, data learning-based evaluation, automated equipment maintenance, actuator diagnosis for manipulator, non-parametric method, distributed diagnosis in multi-agent systems, signal-based anomaly analysis, integrating complex control framework, event knowledge assistance, mobile robot particle filtering method. These topics spanned service robot hardware and software failures, diverse service robot systems, and a range of advanced algorithms for fault detection in service robots. Asia-Pacific, Europe, and the Americas, recognized as three pivotal regions propelling the advancement of service robots, were employed as covariates in this review to investigate regional disparities. The review found that current research tends to favor the use of artificial intelligence algorithms to address service robots’ complex system faults and vast volumes of data. The topics of algorithms, data learning, automated maintenance, and signal analysis are advancing with the support of artificial intelligence, gaining increasing popularity as a burgeoning trend. Additionally, variations in research focus across different regions were found. The Asia-Pacific region tends to prioritize algorithm-related studies, while Europe and the Americas show a greater emphasis on robot safety issues. The integration of diverse technologies holds the potential to bring forth new opportunities for future service robot fault diagnosis. Simultaneously, regional standards about data, communication, and other aspects can streamline the development of methods for service robots’ fault diagnosis.

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Section: Topic Popularity Analysismentioning

confidence: 99%