Unmanned Ground Vehicles (UGVs)-based mobile sensing for Indoor Environmental Quality (IEQ) monitoring: Current challenges and future directions

Alinezhad, Ebrahim; Gan, Victor; Chang, Victor W-C; Zhou, Jin

doi:10.1016/j.jobe.2024.109169

Journal of Building Engineering

2024

DOI: 10.1016/j.jobe.2024.109169

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Unmanned Ground Vehicles (UGVs)-based mobile sensing for Indoor Environmental Quality (IEQ) monitoring: Current challenges and future directions

Ebrahim Alinezhad,

Victor Gan,

Victor W-C Chang

et al.

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2024

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Cited by 2 publications

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The application of sliding innovation filter as estimation strategy applied to a UAV

AlShabi,

Gadsden,

Khuwaileh

et al. 2024

Unmanned Systems Technology XXVI

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The unmanned aerial vehicle (UAV) and unmanned aerial system (UAS) are popular in nowadays applications including military, industry, weather casting, monitoring, and many other applications. According to several research, the system must be controlled in precise way to make sure that the UAV and UAS are moving in the desired trajectories. However, this task is not an easy task in real life due to the presence of disturbances and noise in feedback measurements. To overcome this issue, researchers either developed more stable controllers, i.e. active disturbance rejection control (ADRC), or they improved the measured signals using filters with more accurate/stable performance. This work belongs to the second category, where a newly developed filter, which is referred to as sliding innovation filter (SIF), is used to estimate the states of a UAV system while it is tracking a target at the same height to improve the quality of the controller.

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The application of sliding innovation filter as estimation strategy applied to a UAV

AlShabi,

Gadsden,

Khuwaileh

et al. 2024

Unmanned Systems Technology XXVI

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High-precision indoor localization using the extended Kalman filter approach

AlShabi,

Gadsden,

Obaideen

et al. 2024

Laser Radar Technology and Applications XXIX

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Indoor positioning and navigation have emerged as critical areas of research due to the limitations of GPS in enclosed environments. This study presents an innovative approach to high-precision indoor localization by employing the Extended Kalman Filter (EKF). Unlike traditional methods that often suffer from noise and multi-path effects, the EKF methodology accounts for nonlinearities and offers a recursive solution to estimate the state of dynamic systems. We deployed a sensor on a mobile robot that needs to move in an indoor environment while there is a moving obstacle that is moving around. Our findings demonstrate a significant accuracy in locating the obstacle while maneuvering inside the environment.

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Unmanned Ground Vehicles (UGVs)-based mobile sensing for Indoor Environmental Quality (IEQ) monitoring: Current challenges and future directions

Cited by 2 publications

References 43 publications

The application of sliding innovation filter as estimation strategy applied to a UAV

The application of sliding innovation filter as estimation strategy applied to a UAV

High-precision indoor localization using the extended Kalman filter approach

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