AGV Localization Based on Odometry and LiDAR

Quan, Sibo; Chen, Jianwu

doi:10.1109/wcmeim48965.2019.00102

Cited by 15 publications

(6 citation statements)

References 7 publications

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“…These devices are highly effective when it comes to localizing ad receiving data related to the environment in which the AGV is located. In studies such as the one performed by Quan and Chen [46], these devices are employed in conjunction with the odometry of the wheels in order to localize an autonomous guided vehicle. Despite their extensive use, these sensors do not provide the necessary robustness for this type of systems.…”

Section: Discussionmentioning

confidence: 99%

Dynamical Analysis of a Navigation Algorithm

et al. 2021

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There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.

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Section: Discussionmentioning

confidence: 99%

Dynamical Analysis of a Navigation Algorithm

et al. 2021

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“…[6] achieves high accuracy results by converting the point cloud of LIDAR data into pixel-level depth information and combining it with RGB video data using the commonly used CNN method. [7] established an observation model of AGV based on LiDAR to quickly obtain the accurate position of the vehicle by matching the observation information and achieving the environment awareness during autonomous localization. [8]implemented an AMR robot on TurtleBot3 Burger by combining 2D LIDAR, RGBD camera, and related safety devices LiDAR has its drawbacks, such as high cost and sensitivity to adverse weather conditions.…”

Section: Related Workmentioning

confidence: 99%

EventTube: An Artificial Intelligent Edge Computing Based Event Aware System to Collaborate With Individual Devices in Logistics Systems

Sun

2023

IEEE Trans. Ind. Inf.

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Artificial intelligence has been adopted to facilitate monitoring, operation, and decision in the logistics field. Logistics robots with environment perception capability have been used to improve warehousing efficiency in logistics systems. However, autonomous mobile robots face computationally intensive and real-time demanding tasks such as navigation, localization, and obstacle avoidance. In this paper, we present EventTube, an edge computing-based event aware system that can efficiently discover events from the video data captured by RGB-Monoculars, and collaborate with individual devices to make timely decisions. EventTube deploys a semantic context extraction pipeline on edge servers to aggregate video streams from mobile robots and feed a few keyframes, including the start and end of the specific events to the successive perception pods, accelerating logistics robots' response speed. The event-related model parameters are trained and updated online on a server. The video data collected at the warehouse site for our mobile robots show that EventTube significantly improves parcel delivery efficiency without affecting regular deliveries.

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“…AGV dapat melakukan tugasnya dalam pengangkutan barang menuju tujuan apabila telah mengetahui kondisi lingkungan di sekitarnya. Oleh karena itu, robot AGV perlu melakukan pemetaan dan lokalisasi pada area yang ditentukan sehingga robot dapat bernavigasi ke titik tujuan yang diberikan (Quan & Chen, 2019) (Rufus, dkk, 2020).…”

Section: Pendahuluanunclassified

Simultaneous Localization and Mapping pada Smart Automated Guided Vehicle menggunakan Iterative Closest Point berbasis K-Means Clustering

MARTINI

SUMANTRI²,

Dewantara³

2022

ELKOMIKA

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ABSTRAKAutomated Guided Vehicle (AGV) merupakan salah satu jenis mobile robot yang digunakan untuk mengangkut barang menuju tempat tujuan. AGV mampu bekerja pada lingkungan yang dinamis tanpa menggunakan garis pemandu. Namun sebelumnya harus mempunyai informasi yang cukup terhadap lingkungan kerjanya. Teknik ini dikenal dengan Simulataneous Localization and Mapping (SLAM) yang digunakan robot untuk menggambar peta sekaligus mengetahui posisi robot di dalam peta. Pada penelitian ini, metode yang digunakan yaitu SLAM berbasis Iterative Closest Point (ICP) dengan algoritma K-Means yang menggunakan kumpulan titik dari sensor laser range finder (LRF) untuk membangun peta lingkungan. Pemetaan SLAM menggunakan algoritma K-Means memiliki error hasil scan jarak 77,69% lebih kecil dan waktu eksekusi 0,18% lebih cepat dibandingkan dengan KD-Tree. Peta yang dihasilkan dengan algoritma KMeans pada ICP-SLAM memberikan hasil yang lebih baik & mendekati keadaan ruangan sebenarnya dibandingkan menggunakan algoritma KD-Tree.Kata kunci: ICP-SLAM, K-Means, Laser Range Finder. ABSTRACTAutomated Guided Vehicle (AGV) is a type of mobile robot that is used to transport goods to destination. AGV is able to work in a dynamic environment without guidelines. However, it must have sufficient information about its working environment beforehand. This technique is known as Simultaneous Localization and Mapping (SLAM) which is used by a robot to be able to draw a map as well as to determine its position on the map. In this research, the method used is SLAM based on Iterative Closest Point (ICP) with the K-Means algorithm that uses a collection of points from the Laser Range Finder (LRF) sensor to build an environmental map. SLAM using the K-Means algorithm has 77,69% smaller distance error and 0,18% faster execution time than KD-Tree. The map generated by the K-Means algorithm on an ICP-SLAM gives better results & closer to the actual state than using the KD-Tree. Keywords: ICP-SLAM, K-Means, Laser Range Finder.

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AGV Localization Based on Odometry and LiDAR

Cited by 15 publications

References 7 publications

Dynamical Analysis of a Navigation Algorithm

Dynamical Analysis of a Navigation Algorithm

EventTube: An Artificial Intelligent Edge Computing Based Event Aware System to Collaborate With Individual Devices in Logistics Systems

Simultaneous Localization and Mapping pada Smart Automated Guided Vehicle menggunakan Iterative Closest Point berbasis K-Means Clustering

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