Autonomous mobile robot navigation using machine learning

Song, Xiyang; Fang, Huangwei; Xiong, Jianping; Wang, Ying

doi:10.1109/iciafs.2012.6419894

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…Machine learning (ML) is an artificial intelligence approach that is at the core of these enabling intelligent technologies with widespread adoption [ 2 ] and has become an essential component in accomplishing many intelligent tasks in robotics. The ML techniques incorporate sensor information fusion, object detection [ 48 ], collision avoidance mechanism [ 49 ], pathfinding [ 50 ], path tracking [ 51 ], and control systems [ 21 ] to solve robot autonomous navigation problems [ 52 ]. Diverse arrays of sensors, including laser scanners, cameras, LiDAR, and others, are leveraged for information gathering, mapping, obstacle detection, as well as robot positions and velocities.…”

Section: Concept and Backgroundmentioning

confidence: 99%

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Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Adiuku,

Avdelidis,

Tang

et al. 2024

Sensors

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The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.

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Section: Concept and Backgroundmentioning

confidence: 99%

“…to solve robot autonomous navigation problems [52]. Diverse arrays of sensors, including laser scanners, cameras, LiDAR, and others, are leveraged for information gathering, mapping, obstacle detection, as well as robot positions and velocities.…”

Section: Intelligent Robotic In Mro Hangarmentioning

confidence: 99%

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Adiuku,

Avdelidis,

Tang

et al. 2024

Sensors

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“…It is unlike traditional applications where end user needs to interact with them through simple and structured language such as, "place order", "cancel", "return order", etc. Machine learning is not only used in data science [1] but also in automobile industry [2] biomedical industry, robotics [3], sports [4] etc,. For the past few years, chatbot [5], [6] implementation is a trending concept in service providing industries, e.g., Microsoft's XiaoIce and cortana, Apple Siri, Google Assistant, Amazon's Alexa, etc.…”

Section: Introductionmentioning

confidence: 99%

ChatBot for College Website

Sonawane¹,

Shanmughasundaram²

2019

IJITEE

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In any service-providing organization, it is necessary to answer current or future customer’s questions for their information and for maintaining good relationship with the customers. These organizations usually have call centers with hundreds and thousands of employees to give replies to customers through chat (i.e. text) or phone calls. These kind of traditional systems need large number of employees which eventually increases the overall expenditure and manpower requirement of that organization. This paper is aimed at minimizing the manpower requirement i.e. decreasing the number of employees by implementing a chat service. This concept of chatbot has been proposed for implementation in College Website that will automatically reply to the queries of stakeholders like students, parents, etc. without any human interaction. For implementation of chatbot sequence to sequence model is used which is a deep neural network

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“…To address this limitation, machine learning algorithms (MLs) have been intensively investigated in recent years. In [2,3], neural networks (NNs) were used to determine the viable paths in mobile robots, where ultrasonic sensors or the fusion of sensors and cameras were used to measure the environmental information. In addition, a support vector machine (SVM) was employed for visionbased wheelchair robot navigation [4].…”

mentioning

confidence: 99%

Vision‐based wheelchair navigation using geometric AdaBoost learning

Kim

2017

Electron. lett.

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A novel training algorithm called geometric AdaBoost learning, which integrates the local-appearance models with the explicit shape model is proposed. The proposed algorithm employs a two-stage AdaBoost learning algorithm. The first-stage learning is performed to learn the local texture model within local image patches and to produce a confidence map. Based on the confidence values, the high-discriminative local patches are selected, and then the global context models between them are trained in the later stage using AdaBoost learning. The proposed algorithm is applied to wheelchair navigation, and the results demonstrate that it outperforms the state-of-the-art algorithms with improvements of 23.3 and 49% in terms of accuracy and speed.

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Autonomous mobile robot navigation using machine learning

Cited by 6 publications

References 5 publications

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

ChatBot for College Website

Vision‐based wheelchair navigation using geometric AdaBoost learning

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