Light detection and ranging/inertial measurement unit-integrated navigation positioning for indoor mobile robots

Xiaoyi, Yan; Guo, Hang; Yu, Min; Xu, Yuan; Cheng, Liang; Jiang, Ping

doi:10.1177/1729881420919940

“…Os transportadores autônomos utilizados para realizar a movimentação dos paletes dos setores de montagem até o local de depósito no armazém são empilhadeiras AMR que aplicam o sistema de navegação e orientação através dos sensores LiDAR e da tecnologia SLAM. Portanto, tais transportadores não necessitam de acessórios físicos específicos instalados na área de operação em comparação aos transportadores com a tecnologia AGV, o que torna a programação das trajetórias dos transportadores mais flexível a mudanças de layout (Yan et al, 2020;Walker, 2021). Outro benefício de empregar somente empilhadeiras AMR no sistema logístico automatizado deve-se ao fato de aumentar a versatilidade de utilização dos Portanto, obteve-se o conceito de um sistema logístico automatizado que aplica as ferramentas, equipamentos e recursos mais eficientes e autônomos disponíveis no mercado.…”

Section: Conceito Do Projetounclassified

Projeto conceitual de um sistema logístico automatizado aplicado à indústria moveleira

Velicko

¹

,

Walber²

2022

RSD

0

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A aplicação das tecnologias e conceitos da Indústria 4.0 nos processos logísticos industriais é uma estratégia eficiente para reduzir custos operacionais, agilizar as entregas e aumentar a eficiência do empreendimento. Assim, este trabalho tem como objetivo a realização do projeto conceitual de um sistema logístico automatizado envolvendo a movimentação, armazenagem e gerenciamento dos produtos embalados de uma indústria moveleira. A elaboração deste projeto utilizou as duas primeiras etapas da metodologia de desenvolvimento de projeto proposta por Pahl, Beitz, Feldhusem e Grote (2005), a fase de planejamento e a fase de concepção. Estas etapas permitiram elaborar a lista de requisitos, obter e avaliar as variantes de solução e apresentar o projeto conceitual de um sistema logístico automatizado baseado nas tecnologias da Indústria 4.0 e Logística 4.0 através da identificação dos produtos por radiofrequência e da movimentação por robôs móveis autônomos. Visando a fabricação e implantação deste projeto conceitual são necessárias mais duas etapas da metodologia de Pahl et al. (2005), a fase do projeto preliminar e a fase do projeto detalhado. Estas etapas definirão as especificações técnicas dos equipamentos e o detalhamento da forma de funcionamento dos transportadores, do sistema de armazenagem e do software de gerenciamento, culminando com o layout detalhado do sistema logístico automatizado. Assim, o projeto possui potencial de alavancar o crescimento da indústria moveleira, pois proporcionará um aumento da capacidade de armazenagem e transporte dos produtos de maneira independente da atuação humana e garantirá a rastreabilidade dos produtos em tempo real.

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“…And the attitude information collected by MTi is used to compensate for the posture error caused by the alone Lidar scan matching through RKF, which can improve the positioning accuracy of the direction angle, and also improve the stability of the integrated system. In addition, by adding IMU sensing information, the cumulative errors 14,18 in the process of Lidar data processing are compensated and updated in real time, the fusion algorithm can better perform cumulative errors, it can be seen from Figure 6(c) that the last position (7.5032, 2.9110) the robot returns (the end point) to is very close to the starting point (7.4533, 2.9492). Further quantitative analysis is shown in Table 3.…”

Section: Small-scale Arc Trajectories Environment Test Analysismentioning

confidence: 99%

“…Combining several different navigation systems together can make use of multiple information sources to complement each other for the purpose of forming a multidimensional, multifunctional, and high-accuracy navigation system. At present, the mainstream integration methods are Lidar/ IMU, 14,15 Lidar/Visual, 16 Visual/IMU, 17 Lidar/Visual/ IMU, 18 and so on.…”

Section: Introductionmentioning

confidence: 99%

Novel indoor positioning algorithm based on Lidar/inertial measurement unit integrated system

Jiang

¹

,

Chen

²

,

Guo

³

et al. 2021

International Journal of Advanced Robotic Systems

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As an important research field of mobile robot, simultaneous localization and mapping technology is the core technology to realize intelligent autonomous mobile robot. Aiming at the problems of low positioning accuracy of Lidar (light detection and ranging) simultaneous localization and mapping with nonlinear and non-Gaussian noise characteristics, this article presents a mobile robot simultaneous localization and mapping method that combines Lidar and inertial measurement unit to set up a multi-sensor integrated system and uses a rank Kalman filtering to estimate the robot motion trajectory through inertial measurement unit and Lidar observations. Rank Kalman filtering is similar to the Gaussian deterministic point sampling filtering algorithm in structure, but it does not need to meet the assumptions of Gaussian distribution. It completely calculates the sampling points and the sampling points weights based on the correlation principle of rank statistics. It is suitable for nonlinear and non-Gaussian systems. With multiple experimental tests of small-scale arc trajectories, we can see that compared with the alone Lidar simultaneous localization and mapping algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0928 m to 0.0451 m, with an improved accuracy rate of 46.39%, and the mean error in the Y direction from 0.0772 m to 0.0405 m, which improves the accuracy rate of 48.40%. Compared with the extended Kalman filter fusion algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0597 m to 0.0451 m, with an improved accuracy rate of 24.46%, and the mean error in the Y direction from 0.0537 m to 0.0405 m, which improves the accuracy rate of 24.58%. Finally, we also tested on a large-scale rectangular trajectory, compared with the extended Kalman filter algorithm, rank Kalman filtering improves the accuracy of 23.84% and 25.26% in the X and Y directions, respectively, it is verified that the accuracy of the algorithm proposed in this article has been improved.

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“…A method to accurately locate persons indoors by fusing INS with active RFID was presented in [19]. A method to integrate LiDAR and IMU was proposed to overcome the problem of the low accuracy and large accumulated errors of indoor mobile navigation and positioning [20]. In order to improve the accuracy of the data fusion filter, a tightly coupled UWB/INS-integrated scheme for indoor human navigation was investigated [21].…”

Section: Introductionmentioning

confidence: 99%

Position Estimation and Error Correction of Mobile Robots Based on UWB and Multisensors

Li

¹

,

Xue

²

,

Fu

³

et al. 2022

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Since there are many interferences in the indoor environment, it is difficult to achieve the precise positioning of the mobile robot using a single sensor. This paper presents a position estimation and positioning error correction method of mobile robots based on multisensor data. The robot’s positioning sensor includes ultra-wideband (UWB) components, inertial measurement unit (IMU), and encoders. UWB multipath interference causes more ranging errors, which can be reduced by the correction equation after data fitting. The real-time coordinates of the UWB robot tag can be calculated based on multiple UWB anchor data and the least squares method. The coordinate data x c , y c are acquired by UWB positioning subsystem, and the velocity data x ̇ c , y ̇ c are collected by IMU together with encoders. The multisensor data continuously update Kalman filter and estimate robot position. In the positioning process, the positioning data of different sensors can be mutually corrected and supplemented. The results of UWB ranging correction experiments indicate that data fitting can improve the UWB positioning accuracy. In the multisensor positioning experiments, compared with a single sensor, the positioning method based on data fusion of UWB, IMU, and encoders has higher accuracy and adaptability. When UWB signals are interfered or invalid, other sensors can still work normally and complete the robot positioning process. The multisensor positioning method not only improves the robot positioning accuracy but also has stronger environmental adaptability.

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Light detection and ranging/inertial measurement unit-integrated navigation positioning for indoor mobile robots

Cited by 9 publications

References 22 publications

Projeto conceitual de um sistema logístico automatizado aplicado à indústria moveleira

Projeto conceitual de um sistema logístico automatizado aplicado à indústria moveleira

Novel indoor positioning algorithm based on Lidar/inertial measurement unit integrated system

Position Estimation and Error Correction of Mobile Robots Based on UWB and Multisensors

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