Multi-Ray Modeling of Ultrasonic Sensors and Application for Micro-UAV Localization in Indoor Environments

Yang, Lingyu; Feng, Xiao; Zhang, Jing; Shu, Xiangqian

doi:10.3390/s19081770

Cited by 24 publications

(15 citation statements)

References 15 publications

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“…For a smooth, fast, and reliable UAV movement, regardless of the perceptual sensors that has been used to control it, such as GPS [4], optical flow [5], ultrasonic sensors [6] and laser range finder [7], the controller should manipulate the actuators in different environmental situations during the mission and landing periods. These environmental conditions are accompanied by noise and in some cases, such as the period of landing the UAV, affects the dynamics of the UAV and must be considered in control design in runtime.…”

Section: Introductionmentioning

confidence: 99%

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

et al. 2022

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In this paper, an adaptable fuzzy control mechanism for an Unmanned Aerial Vehicle (UAV) to manipulate its mechanical actuators is provided. The mission (landing) for the UAV is defined to track (land on) an object that is detected by a deep learning object detection algorithm. The inputs of the controller are the location and speed of the UAV that has been calculated based on the location of the detected object.Two separate fuzzy controllers are proposed to control the UAV motor throttle and its roll and pitch over the mission and landing time. Fuzzy logic controller (FLC) is an intelligent controller that can be used to compensate for the non-linearity behaviour of the UAV by designing a specific fuzzy rule base . These rules will be utilized to adjust the control parameters during the mission and landing period in runtime.To add the effect of the ground for tuning the FLC membership function over the landing operation, a computational flow dynamic (CFD) modeling has been investigated. The proposed techniques is evaluated on MATLAB/Simulink simulation platform and real environment. Statistical analysis of the UAV location reported during stabilization and landing process, on both simulation and real platform, shows that the proposed technique outperforms the similar state-of-art control techniques for both mission and landing control.

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Section: Introductionmentioning

confidence: 99%

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

et al. 2022

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“…Several recent studies have focused on improving drone localisation in confined spaces [27][28][29][30][31] to overcome previously mentioned limitations. Chen et al [27] presented a visual SLAM with a time-of-flight (ToF) camera.…”

Section: Challenges Of Flying Drones In Confined Spacesmentioning

confidence: 99%

“…The presented work used an EKF to fuse the sonar sensor with an IMU to estimate the drone location in an indoor area. The sonar sensor detection range was limited to 6 m. Zhang et al [31] estimated drone positions and orientations in an indoor area by attaching the drone with three or more ultra-high frequency tags, and a radio-frequency identification reader was used to track the drone using a Bayesian filter-based algorithm. Test results showed 0.04 m average position error and 2.5 • average orientation error from the actual positions and orientation.…”

Section: Challenges Of Flying Drones In Confined Spacesmentioning

confidence: 99%

Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation

et al. 2021

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The accuracy of stockpile estimations is of immense criticality to process optimisation and overall financial decision making within manufacturing operations. Despite well-established correlations between inventory management and profitability, safe deployment of stockpile measurement and inspection activities remain challenging and labour-intensive. This is perhaps owing to a combination of size, shape irregularity as well as the health hazards of cement manufacturing raw materials and products. Through a combination of simulations and real-life assessment within a fully integrated cement plant, this study explores the potential of drones to safely enhance the accuracy of stockpile volume estimations. Different types of LiDAR sensors in combination with different flight trajectory options were fully assessed through simulation whilst mapping representative stockpiles placed in both open and fully confined areas. During the real-life assessment, a drone was equipped with GPS for localisation, in addition to a 1D LiDAR and a barometer for stockpile height estimation. The usefulness of the proposed approach was established based on mapping of a pile with unknown volume in an open area, as well as a pile with known volume within a semi-confined area. Visual inspection of the generated stockpile surface showed strong correlations with the actual pile within the open area, and the volume of the pile in the semi-confined area was accurately measured. Finally, a comparative analysis of cost and complexity of the proposed solution to several existing initiatives revealed its proficiency as a low-cost robotic system within confined spaces whereby visibility, air quality, humidity, and high temperature are unfavourable.

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“…The use of acoustic signals [ 1 , 8 , 9 , 10 ] to determine the position of the device allows accuracies in the range of centimeters, or even sub-centimeters in reduced areas of a few meters, with low-cost transducers. The main drawback is that an infrastructure installation is required (usually composed of several transducers) to estimate the position of the target.…”

Section: Introductionmentioning

confidence: 99%

LOCATE-US: Indoor Positioning for Mobile Devices Using Encoded Ultrasonic Signals, Inertial Sensors and Graph-Matching

Gualda

Pérez-Rubio

Ureña

et al. 2021

Sensors

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Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (U-LPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others.

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Multi-Ray Modeling of Ultrasonic Sensors and Application for Micro-UAV Localization in Indoor Environments

Cited by 24 publications

References 15 publications

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation

LOCATE-US: Indoor Positioning for Mobile Devices Using Encoded Ultrasonic Signals, Inertial Sensors and Graph-Matching

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