Cost Effective and Easily Configurable Indoor Navigation System

UKH Journal of Science and Engineering

Mohammed Yaseen Taha¹  Qahhar Muhammad Qadir¹ 

[1] Department of Electrical Engineering, College of Engineering, Salahaddin University-Erbil, Erbil, Iraq;

关键词: industry 4.0;    global positioning system(gps);    indoor positioning system(ips);    unmanned ground vehicle(ugv);    artificial intelligence(ai);    computer vision;    deep machine learning;   

DOI  :  https://doi.org/10.25079/ukhjse.v5n1y2021.pp60-72

来源: DOAJ

【 摘 要 】

With the advent of Industry 4.0, the trend of its implementation in current factories has increased tremendously. Using autonomous mobile robots that are capable of navigating and handling material in a warehouse is one of the important pillars to convert the current warehouse inventory control to more automated and smart processesto be aligned with Industry 4.0 needs. Navigating a robot’s indoor positioning in addition to finding materialsareexamples of location-based services (LBS), and are some major aspects of Industry4.0 implementation in warehouses that shouldbeconsidered.Globalpositioningsatellites(GPS) areaccurateandreliableforoutdoornavigationand positioning while they are not suitable forindooruse. Indoor positioning systems(IPS) havebeen proposed in order toovercomethis shortcomingandextendthisvaluableservicetoindoor navigationandpositioning. Thispaper proposes a simple, cost effective and easily configurable indoor navigation system with the help of an optical path following,unmannedgroundvehicle(UGV)robotaugmentedbyimageprocessingandcomputervisiondeep machine learning algorithms. The proposed system prototype is capable of navigating in a warehouse as an example of an indoor area, by tracking and following a predefined traced path that covers all inventory zones in a warehouse, throughtheusageofinfraredreflectivesensorsthatcandetectblacktracedpathlinesonbrightground.As metiondedbefore,thisgeneralnavigationmechanismisaugmentedandenhancedbyartificialintelligence(AI) computer vision tasks to be able to select the path to the required inventory zone as its destination, and locatethe requested material within this inventory zone. The adopted AI computer vision tasks that are used in the proposed prototypearedeepmachinelearningobjectrecognitionalgorithmsforpathselectionandquickresponse(QR) detection.

【 授权许可】

Unknown