Precise Localization of Mobile Robots via Odometry and Wireless Sensor Network

Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin

doi:10.5772/56217

Cited by 24 publications

(19 citation statements)

References 24 publications

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“…Penggunaan metode odometry pada sistem navigasi AMR adalah salah satu jawaban pada persoalan keterbatasan resolusi GPS. Penelitian AMR dengan metode odometry antara lain telah dilaksanakan oleh [5], [6], dan [7]. Pada penelitan AMR yang pertama dikembangkan metode odometry digabung dengan sensor kamera, penelitian kedua odometry dan sensor ultrasonic LPS (Local Positioning System) serta penelitian ketiga menggabungkan odometry dan Wireless Sensor Network (WSN).…”

Section: Pendahuluanunclassified

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Robot Mobile Otonom Menggunakan Metode Odometry

Rakhman

Basjaruddin

Susanto

2019

tech

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Robot mobile otonom (autonomous mobile robot, AMR) banyak digunakan baik di industri maupun lingkungan rumah tangga. Robot jenis ini mempunyai kemampuan bergerak menuju tempat tertentu secara otomatis. Penggunaan GPS pada tempat tertutup dan area terbatas belum dapat memenuhi kebutuhan karena keterbatasan resolusi GPS. Pada penelitian ini dikembangkan robot mobile yang mampu menuju titik tertentu dengan memanfaatkan sensor rotary encoder dan metode odometry. Koordinat tujuan diberikan ke robot dan kemudian secara otomatis robot bergerak ke arah titik koordinat tujuan. Pengujian dilakukan dengan memberikan tujuan dengan jarak berbeda yaitu 60 cm, 100 cm, 150 cm, 200 cm, dan 250 cm. Rata-rata galat perhitungan odometry yang dihitung pada jarak tersebut adalah sebesar 0.5 cm, 2.63 cm, 1.65 cm, dan 1.1 cm. Pada pengukuran secara riil didapatkan galat sebesar 7cm, 13cm, 10cm, dan 20 cm. Pengujian rute langsung mempunyai nilai galat yang paling kecil dibanding pengujian rute 1 dan rute 2 yaitu 0,11 cm untuk tujuan x dan 0,25 cm untuk tujuan y. Pada pengukuran posisi riil mempunyai galat x = 3 cm dan y = 2 cm.

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Section: Pendahuluanunclassified

“…(9). Sedangkan, Posisi Mobil terjadi di titik (Xposisi, Yposisi) seperti pada persamaan (6) dan (7). Penentuan posisi mobil pada metode ini bersifat kendali lingkar terbuka karena tidak dilengkapi dengan umpan balik bahwa posisi sudah berada pada koordinat tujuan.…”

Section: Diagram Blok Dan Cara Kerjaunclassified

Robot Mobile Otonom Menggunakan Metode Odometry

Rakhman

Basjaruddin

Susanto

2019

tech

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“…Due to this cumulative errors it is common to combine different localization methods. Several works can be pointed out using the odometry localization with different approches, such as image processing [13] or Wireless Sensor Network [14].…”

Section: Related Workmentioning

confidence: 99%

Ultra-Wideband Time of Flight Based Localization System and Odometry Fusion for a Scanning 3 DoF Magnetic Field Autonomous Robot

Lima¹,

Costa²

2017

ROBOT 2017: Third Iberian Robotics Conference

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Abstract. Solving the robot localization problem is one of the most necessary requirements for autonomous robots. Several methodologies can be used to determine its location as accurately as possible. What makes this difficult is the existence of uncertainty in the sensing of the robot. The uncertain information needs to be combined in an optimal way. This paper stresses a Kalman filter to combine information from the odometry and Ultra Wide Band Time of Flight distance modules, which lacks the orientation. The proposed system validated in a real developed platform performs the fusion task which outputs position and orientation of the robot. It is used to localize the robot and make a 3 DoF scanning of magnetic field in a room. Other examples can be pointed out with the same localization techniques in service and industrial autonomous robots.

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“…This method of arranging vision and RFID sensors is done in order to improve the accuracy, reliability and real-time performance of lowcost guidance systems. Many attempts have been made to improve the accuracy of robot location by using the RFID technique or combining it with other techniques, e.g., utilizing the read time for the antenna to detect a tag in a gird-like pattern of many passive RFID tags (the average location error is about 6 cm for a grid-like pattern of 4.2m×6.2m with a spacing of 34 cm) [17], or by fusing the location data of the odometry and wireless sensor network to deal with the unbounded accumulated error of dead reckoning (the average square deviation is about 0.81 m 2 for an area of 30m×30m) [18]. Its accuracy and repeatability (in a large area) are not sufficient for industrial applications.…”

Section: System Configurationmentioning

confidence: 99%

Intersection Recognition and Guide-Path Selection for a Vision-Based AGV in a Bidirectional Flow Network

Lou

et al. 2014

International Journal of Advanced Robotic Systems

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Vision recognition and RFID perception are used to develop a smart AGV travelling on fixed paths while retaining low-cost, simplicity and reliability. Visible landmarks can describe features of shapes and geometric dimensions of lines and intersections, and RFID tags can directly record global locations on pathways and the local topological relations of crossroads. A topological map is convenient for building and editing without the need for accurate poses when establishing a priori knowledge of a workplace. To obtain the flexibility of bidirectional movement along guide-paths, a camera placed in the centre of the AGV looks downward vertically at landmarks on the floor. A small visual field presents many difficulties for vision guidance, especially for realtime, correct and reliable recognition of multi-branch crossroads. First, the region projection and contour scanning methods are both used to extract the features of shapes. Then LDA is used to reduce the number of the features' dimensions. Third, a hierarchical SVM classifier is proposed to classify their multi-branch patterns once the features of the shapes are complete. Our experiments in landmark recognition and navigation show that lowcost vision systems are insusceptible to visual noises, image breakages and floor changes, and a vision-based AGV can locate itself precisely on its paths, recognize different crossroads intelligently by verifying the conformance of vision and RFID information, and select its next pathway efficiently in a bidirectional flow network.

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Precise Localization of Mobile Robots via Odometry and Wireless Sensor Network

Cited by 24 publications

References 24 publications

Robot Mobile Otonom Menggunakan Metode Odometry

Robot Mobile Otonom Menggunakan Metode Odometry

Ultra-Wideband Time of Flight Based Localization System and Odometry Fusion for a Scanning 3 DoF Magnetic Field Autonomous Robot

Intersection Recognition and Guide-Path Selection for a Vision-Based AGV in a Bidirectional Flow Network

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