A study of Monte Carlo localization on robot operating system

Aini, Fitria Romadhona Quratul; Jati, Agung Nugroho; Sunarya, Unang

doi:10.1109/icitsi.2016.7858235

Cited by 11 publications

(5 citation statements)

References 2 publications

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“…Generally, the default method of robot localization provided by the stack move_base inside the ROS is AMCL, (19) which is a probabilistic localization system for a robot moving in 2D space. Owing to its probabilistic concept, AMCL can recorrect the position of a robot better than only encoder-based odometry.…”

Section: Improvement Of Localizationmentioning

confidence: 99%

Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line

Yanyong¹,

Parichatprecha²,

Chaisiri³

et al. 2023

Sensors and Materials

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In this paper, the adaptive Monte Carlo localization (AMCL) error in terms of similar data detected by light detection and ranging (LiDAR) in different locations is investigated. This localization causes a robot to move to the incorrect location temporarily. We propose the fusion of landmark-based localization using an iBeacon device combined with the AMCL algorithm. This technique can solve the probabilistic localization problem of the conventional techniques applied in mobile robots by fusing the timed elastic band (TEB) and scan-matching algorithms, which reduces the error from 7 cm to less than 3 cm. The proposed technique is implemented on a clean-room-type mobile robot with 100 kg payload certificated by the SOP39 standard.

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Section: Improvement Of Localizationmentioning

confidence: 99%

Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line

Yanyong¹,

Parichatprecha²,

Chaisiri³

et al. 2023

Sensors and Materials

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show abstract

“…Synchronously, the combination of 3D point cloud map, generated by vision SLAM, and the occupancy grid map (OGM), constructed by 2D laser scanner, were used to form a new fusion point cloud-occupancy grid map (PC-OGM). In the localization module, based on the above PC-OGM, EKF fusion method is used again, which further combines the pose estimation of vision SLAM and the positioning estimation based on 2D laser-based adaptive Monte Carlo localization (AMCL) [22] [29] to achieve robust and complementary positioning performance. For the navigation module, the application of an adaptive motion controller was proposed.…”

Section: ) Control Core Layermentioning

confidence: 99%

Non-Contact Service Robot Development in Fast-Food Restaurants

2022

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Given the prospect of low birth rate and aging population among developed countries, which further resulted in the shortage of workforces, service robots have been gradually applied to reallife from past academic research. This paper introduces the development of a service robot that is designed for food service in fast-food restaurants with the innovative improvement of mapping, localization, and navigation. Moreover, this research took the initiative of integrating 3D point cloud map and 2D occupancy grid map (OGM) in order to build a PC-OGM. In another word, using the sensory fusion method allows the service robot to adapt to a more complicated environment as well as enhance its positioning. In terms of its navigation function, the adaptive motion controller is refined so the service robot could navigate through narrow aisles smoothly. Finally, friendly contact-free food service robots were evaluated at fastfood restaurants in order to gain feedback from diners and waiters. Their feedback was broken down into 3 categories, availability, reliability, and satisfaction, for further analysis. As COVID-19 assails the world, we also look into future possibilities of food service robot deployment among restaurants to keep food and surface free from the virus.

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“…Como consecuencia del ruido existente en estos sensores, se calcula la función de densidad para todas las posibles poses partiendo de un estado inicial donde convergen hacia una zona de interés. Este método se conoce como aproximación de localización de Monte Carlo, AMCL por sus siglas en inglés [12].…”

Section: Localización Y Poseunclassified

Análisis de algoritmos de navegación para una flota de robots móviles mediante gemelos digitales

2022

Congreso Iberoamericano De Ingeniería Mecánica-Cibim 2022

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La manutención industrial se encuentra en un proceso de digitalización y automatización mediante el uso de tecnologías de la industria 4.0. Un ejemplo son los gemelos digitales los cuales permiten virtualizar activos como la flota de Robots Móviles Autónomos (RMA). En este entorno digitalizado son analizados dos algoritmos de navegación local; Aproximación de Ventana Dinámica (DWA) y el Tiempo de Banda Elástico (TEB). Para ello, se implementa un gemelo digital en el ecosistema ROS donde se virtualiza un entorno industrial. En este entorno es simulada una misión de una flota de tres RMA donde navegan conjuntamente con el fin de comparar el comportamiento de los algoritmos en entornos industriales dinámicos. Como resultado, se analizan las trayectorias y los tiempos de cada RMA que son clave para la planificación de los costes de producción. Además, en este trabajo se desarrolla un servidor web para interactuar con el gemelo digital facilitando su uso.

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A study of Monte Carlo localization on robot operating system

Cited by 11 publications

References 2 publications

Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line

Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line

Non-Contact Service Robot Development in Fast-Food Restaurants

Análisis de algoritmos de navegación para una flota de robots móviles mediante gemelos digitales

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