A Low Cost Ultrasonic Based Positioning System for the Indoor Navigation of Mobile Robots

SUMMARYThis paper proposes methodologies and techniques for multi-block navigation of an indoor localization system with active beacon sensors. As service robots and ubiquitous technology have evolved, there is an increasing need for autonomous indoor navigation of mobile robots. In a large number of indoor localization schemes, the absolute position estimation method, relying on navigation beacons or landmarks, has been widely used due to its low cost and high accuracy. However, few of these schemes have managed to expand the applications for use in complicated workspaces involving many rooms or blocks that cover a wide region, such as airports and stations. Since the precise and safe navigation of mobile robots in complicated workspaces is vital for the ubiquitous technology, it is necessary to develop a multi-block navigation scheme. This new design of an indoor localization system includes ultrasonic attenuation compensation, dilution of both the precision analysis and fault detection, and an isolation algorithm using redundant measurements. These ideas are implemented on actual mobile robot platforms and beacon sensors, and experimental results are presented to test and demonstrate the new methods.

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Autonomous Indoor Navigation for Mobile Robots

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g9038.0510721 ◽

2021 ◽

Vol 10 (7) ◽

pp. 122-126

Author(s):

Karthik C* Valliappan ◽

Vikram R

Keyword(s):

Mobile Robots ◽

Navigation System ◽

Reference Point ◽

Autonomous Navigation ◽

Low Cost ◽

Image Sensor ◽

Indoor Navigation ◽

Reference Points ◽

Field Of View ◽

Robot Position

An autonomous navigation system for a robot is key for it to be self-reliant in any given environment. Precise navigation and localization of robots will minimize the need for guided work areas specifically designed for the utilization of robots. The existing solution for autonomous navigation is very expensive restricting its implementation to satisfy a wide variety of applications for robots. This project aims to develop a low-cost methodology for complete autonomous navigation and localization of the robot. For localization, the robot is equipped with an image sensor that captures reference points in its field of view. When the robot moves, the change in robot position is estimated by calculating the shift in the location of the initially captured reference point. Using the onboard proximity sensors, the robot generates a map of all the accessible areas in its domain which is then used for generating a path to the desired location. The robot uses the generated path to navigate while simultaneously avoiding any obstacles in its path to arrive at the desired location.

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Performance Evaluation of RSSI-based Positioning System with Low-cost LoRa Devices

Proceedings of the 16th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks - PE-WASUN '19 ◽

10.1145/3345860.3361512 ◽

2019 ◽

Author(s):

Christian Sanchez ◽

Braulio Arpi ◽

Andres Vazquez-Rodas ◽

Fabian Astudillo-Salinas ◽

Luis I. Minchala

Keyword(s):

Performance Evaluation ◽

Low Cost ◽

Positioning System

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Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Robotica ◽

10.1017/s0263574720001526 ◽

2021 ◽

pp. 1-18

Author(s):

Majid Yekkehfallah ◽

Ming Yang ◽

Zhiao Cai ◽

Liang Li ◽

Chuanxiang Wang

Keyword(s):

Mobile Robots ◽

Inertial Measurement Unit ◽

Indoor Environment ◽

State Of The Art ◽

Low Cost ◽

Estimation Algorithm ◽

Measurement Unit ◽

3D Localization ◽

Fast Motion ◽

Tof Camera

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

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Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments

Sensors ◽

10.3390/s21062218 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2218

Author(s):

Sizhen Bian ◽

Peter Hevesi ◽

Leif Christensen ◽

Paul Lukowicz

Keyword(s):

Magnetic Field ◽

Low Cost ◽

Autonomous Underwater Vehicles ◽

Wide Band ◽

Propagation Delay ◽

Positioning System ◽

Indoor Environments ◽

Coverage Area ◽

Underwater Positioning ◽

Similar Accuracy

Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration but are still restricted by the availability of short range, accurate positioning methods necessary, e.g., when docking remote assets. Typical techniques used for high-accuracy positioning in indoor use case scenarios, such as systems using ultra-wide band radio signals (UWB), cannot be applied for underwater positioning because of the quick absorption of the positioning medium caused by the water. Acoustic and optic solutions for underwater positioning also face known problems, such as the multi-path effects, high propagation delay (acoustics), and environmental dependency. This paper presents an oscillating magnetic field-based indoor and underwater positioning system. Unlike those radio wave-based positioning modalities, the magnetic approach generates a bubble-formed magnetic field that will not be deformed by the environmental variation because of the very similar permeability of water and air. The proposed system achieves an underwater positioning mean accuracy of 13.3 cm in 2D and 19.0 cm in 3D with the multi-lateration positioning method and concludes the potential of the magnetic field-based positioning technique for underwater applications. A similar accuracy was also achieved for various indoor environments that were used to test the influence of cluttered environment and of cross environment. The low cost and power consumption system is scalable for extensive coverage area and could plug-and-play without pre-calibration.

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Constructing Floor Plan through Smoke Using Ultra Wideband Radar

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3494977 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-29

Author(s):

Weiyan Chen ◽

Fusang Zhang ◽

Tao Gu ◽

Kexing Zhou ◽

Zixuan Huo ◽

...

Keyword(s):

Spatial Information ◽

Low Cost ◽

Indoor Navigation ◽

Location Based Services ◽

Ultra Wideband ◽

Indoor Environments ◽

Floor Plan ◽

Construction Methods ◽

Median Error ◽

Indoor Scenarios

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

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