Factory Automation: Resource Allocation of an Elevated LiDAR System with URLLC Requirements

Jayaweera, Nalin; Marasinghe, Dileepa; Rajatheva, Nandana

doi:10.1109/6gsummit49458.2020.9083914

Cited by 14 publications

(4 citation statements)

References 7 publications

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“…With the rotation matrix estimated, next step is to estimate the parameters for linear translation. This starts by rotating the point cloud using the estimated rotation matrix R in (5). Let us denote the rotated point cloud as an intermediate point cloud M. The two point clouds will now only have a linear offset between them.…”

Section: B Translation Parametersmentioning

confidence: 99%

“…However, sharing sensor information between mobile robots introduces a heavy burden on the communication links as those sensing technologies produce a huge amount of data which is in Gbps range. In our previous works, we proposed an infrastructure based sensing architecture with a coordinated set of elevated LiDARs (ELiDs) which develops a digital twin of the environment [4], [5]. This architecture has the potential of delivering a solution to the positioning problem while overcoming the issue of limited perception of the environment to the robot.…”

Section: Introductionmentioning

confidence: 99%

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Elevated LiDAR based Sensing for 6G -- 3D Maps with cm Level Accuracy

Padmal¹,

Marasinghe²,

Vijitha³

et al. 2021

Preprint

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One key vertical application that will be enabled by 6G is the automation of the processes with the increased use of robots. As a result, sensing and localization of the surrounding environment becomes a crucial factor for these robots to operate. Light detection and ranging (LiDAR) has emerged as an appropriate method of sensing due to its capability of generating detail-rich information with high accuracy. However, LiDARs are power hungry devices that generate a lot of data, and these characteristics limit their use as on-board sensors in robots. In this paper, we present a novel approach on the methodology of generating an enhanced 3D map with improved field-of-view using multiple LiDAR sensors. We utilize an inherent property of LiDAR point clouds; rings and data from the inertial measurement unit (IMU) embedded in the sensor for registration of the point clouds. The generated 3D map has an accuracy of 10 cm when compared to the real-world measurements. We also carry out the practical implementation of the proposed method using two LiDAR sensors. Furthermore, we develop an application to utilize the generated map where a robot navigates through the mapped environment with minimal support from the sensors on-board. The LiDARs are fixed in the infrastructure at elevated positions. Thus this is applicable to vehicular and factory scenarios. Our results further validate the idea of using multiple elevated LiDARs as a part of the infrastructure for various applications.

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Section: B Translation Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Elevated LiDAR based Sensing for 6G -- 3D Maps with cm Level Accuracy

Padmal¹,

Marasinghe²,

Vijitha³

et al. 2021

Preprint

Self Cite

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show abstract

“…Motivated by these factors and the ability to provide detailed yet privacy-persevered data, we employ LiDAR as the sensing modality in this work. We consider an infrastructuremounted LiDAR system following our previous works [4], [5], [6] for beam prediction assisting a 5G NR system. We focus on predicting the future downlink beams while reducing the frequency of the beam reports, thus reducing the resource usage for beam measurements.…”

Section: Introductionmentioning

confidence: 99%

LiDAR aided Wireless Networks - Beam Prediction for 5G

Marasinghe

Jayaweera

Rajatheva

et al. 2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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5G New Radio (NR) mmWave operates with narrow beams. Beam-based connections require careful management of beams to ensure a reliable connection, specially when the user has mobility. 5G NR beam management achieve this, at the expense of periodic reporting with increased overheads and resource usage. Concurrently, recent interest in sensing for assisting wireless systems provides an opportunity to extract situational awareness information which can aid in proactive decisions for the network. In this work, we utilize an infrastructure-mounted light detection and ranging (LiDAR) sensor system simultaneously operating with the wireless system to predict future beam decisions. A recurrent neural network (RNN) based learning model is proposed for the beam prediction, employing tracking information of users facilitated by the LiDARs and beam sequence information from the wireless system. Furthermore, a method for predictive beam management with increased periodicity of the reporting mechanism and aperiodic reporting is analyzed. The results for the considered scenario reveal 86.8% of the resources can be saved compared to the conventional beam reporting procedure, while achieving an 88.7% accuracy for optimal beam decisions.

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“…possible applications of radar sensing, such as detection of drones [5]- [7], and factory automation [8]. Such applications also require a reliable communication infrastructure to safely and seamlessly operate, and hence, are expected to be incorporated into mobile networks [9,10].…”

mentioning

confidence: 99%

A Dual-Function Massive MIMO Uplink OFDM Communication and Radar Architecture

Temiz

Alsusa

Baidas

2022

IEEE Trans. Cogn. Commun. Netw.

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This paper proposes a joint uplink massive multiple-input-multiple-output (MIMO) communication and orthogonal frequency-division multiplexing (OFDM) radar sensing architecture. Specifically, uplink communication and short-range radar sensing are considered, where the user equipments (UEs) transmit data to the base-station (BS), which simultaneously receives radar returns from the targets over the same subcarriers. Hence, the signals received at each BS antenna include radar returns and communication signals to be processed for extracting the sensing and communication data. The separation and detection of such signals are achieved by utilizing the channel diversity between the UEs and the targets. To this end, the UEs' signals are first detected and demodulated, and then subtracted from the received signal to acquire the radar returns. Symbol-based radar processing is then employed, as it provides substantial processing gains, and its detection performance is independent of the transmitted radar waveform. Furthermore, self-interference-due to the simultaneous operation of transmit and receive antennas-is taken into account. The communication capacity and normalized error of the radar-target channel estimation are mathematically analyzed, and the trade-off between the communication capacity and radar detection performance is demonstrated in terms of the power output of the communication and radar sub-systems, as well as the signal-to-noise ratio.

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Factory Automation: Resource Allocation of an Elevated LiDAR System with URLLC Requirements

Cited by 14 publications

References 7 publications

Elevated LiDAR based Sensing for 6G -- 3D Maps with cm Level Accuracy

Elevated LiDAR based Sensing for 6G -- 3D Maps with cm Level Accuracy

LiDAR aided Wireless Networks - Beam Prediction for 5G

A Dual-Function Massive MIMO Uplink OFDM Communication and Radar Architecture

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