Indoor Localization of Mobile Robots with Wireless Sensor Network Based on Ultra Wideband using Experimental Measurements of Time Difference of Arrival  Ousmane Abdoulaye Oumar School of Engineering London South Bank University Thank you. This paper work is on Localization of Mobile Robots with Wireless Sensor Network Based on Ultra Wideband Experimental measurements of TDOA. This work presented in this paper carries out experimental validation of localization algorithms using mobile robots and UWB signals. These were measured in LOS and NLOS environments. The measurements are performed with the UWB radio PulsON 410 (P410) and mobile robots (Ami-goBot) travelling at maximum speed of 1 m/s and equipped with an on-board computer, sonar, odometer, camera and inertial navigation system.

Outline Overview Aim and Motivation Different methods Materials PulsON 410 system, which is formed by tags (transmitters / receivers) Four sensors (fixed nodes), which are constantly trans-mitting signals, and the reflected signals from a robot moving in an indoor environment are collected by each of these sensors. The AmigoBot contains all of the basic components for autonomous sensing and navigation in a real-world environment, including battery power, drive motors and wheels, position/speed encoders, sonar range finding sensors, and integrated accessories, all managed via an on-board micro controller and mobile robot server software. The frequency band allocated by the Federal Communications Commission (FCC) to UWB ranges from 3.1 GHz to 10.6 GHz in single band or multiband. Experimental results Simulation results using TDOA method Estimation of TDOA and measurement environment Advantages and benefits of UWB Conclusions & Future work Here is the outline. I will first provide an overview & motivation. Then, I will present my work. It includes two components, one is theoretical, the other is experimental. Finally I will summarize my work.

Overview Investigated indoor localization techniques using UWB. This work carries out experimental validation of localization algorithms using mobile robots and UWB signals. These were measured in line of sight (LOS) or non-line of sight or direct signal path (NLOS) environments. The measurements are performed with the UWB radio PulsON 410 (P410) and mobile robots (AmigoBot) travelling at maximum speed of 1 m/s and equipped with an onboard computer, sonar, odometer, camera and inertial navigation system. UWB localization is superior in terms of accuracy and power consumption compared with general positioning system (GPS) and wireless local area network (WLAN) localization. It is thus more suitable for most indoor location-based applications.

Aim and Motivation The aim of this work is to develop wireless localization techniques: Outdoor and indoor Accuracy Less power consumption Small size Low equipment cost effective and low complexity Immunity to multipath and interference Analysis the different methods, choose the best two method suitable, low cost techniques and simulations Finally validate the algorithms of estimation by using experimental UWB signals

Different methods The different methods of estimation and performance of the algorithms.

. Figure 2 A: The different systems in ultra-high and super high frequency band Figure 2 B: Illustration of the spectrum of UWB signal versus narrowband signal

Simulation results using TDOA method Simulation of TDOA localization technique is performed to calculate the 3D positions of a target node and multiple target nodes. The error in the TDOA localization method is defined to be the difference between the true location and the estimated location of the target node. TDOA and RSS techniques are the best choices for the indoor and outdoor localization. The TOA technique also requires that the observers have a very stable and accurate clock and requires synchronization of the target and sensor nodes’ clocks. But TDOA doesn’t requires synchronization and large position errors can occur if the time synchronization between the mobile and base stations is inaccurate. For example, a simple clock inaccuracy of just 1 nanosecond will lead to a position error of 30 cm and a timing error of 1 microsecond corresponds to a ranging error of 300 m. Figure 3‑: TDOA based position location with four reader nodes and 1 and 50 actual and estimated target nodes.

Simulation results for RSS Eequations are used to calculate the received power (RSS) Different values of carrier frequency ranging from 1 to 10 GHz. The received power is calculated at distances between the transmitter and receiver ranging from 1 to 30m. The results show that the rate of power decrease with distance is the same at all frequencies. The average power decreases over the distance with a rate of 3 to 11 dB.

Figure 4: Received power against distance (1 to 30 m) at 1 to 10 GHz. A: Received power against distance (1 to 30 m) at 1 GHz. B: Received power against distance (1 to 30 m) at 4 GHz. C: Received power against distance (1 to 30 m) at 7 GHz. D: Received power against distance (1 to 30 m) at 10 GHz. Figure 4: Received power against distance (1 to 30 m) at 1 to 10 GHz.

Figure 5: Received signal power at distances of 1 to 30 m at 1-10 GHz. RSS localization is a simple and efficient positioning algorithm. RSS localization offers a low cost implementation. The accuracy of the RSS method is not a very high, but is better than AOA,etc. The implementation costs are minimal and this is the advantage of RSS based positioning.

Indoor localization with UWB using TDOA experimental measurements . Figure 6: PulsON 410 with antenna Figure 7: Three PulsOn 410 UWB sensors (each PulsOn 410 is in an enclosure). UWB pulses are transmitted from the sensors at every millisecond by the transmitter and all reflected signals are picked up by the receiver. UWB signals are characterized by the transmission of a few nanosecond duration pulses, they have very high time resolution and localization precision, which make UWB sensors an ideal equipment for short range radar sensor network applications . In this study, UWB sensors are employed for detecting and tracking multiple moving objects in an indoor environment in the context of passive localization. Figure 8: Control window showing the signal on a laptop.

. This Figure is a viewing window of the waveform of the received signal. This window is automatically displayed after the activation of the capture function in the command window. In addition, the viewing window is used for: Observation and analysis point by point in the waveform of the received signal Observation of multiple signals on the same window Observation of the signal with zoom along the axes Figure 9: Tab layout convention illustrating the radio mode, range, etc.

The AmigoBot with P410 . The AmigoBot is fitted with an internal coordinate system. It has two encoders, one fitted to each motor shaft and these are used as an odometer to estimate the robot’s position and orientation relative to its starting position. A very precise localization in a mapped space using robot dometry combined with laser rangefinder data is implemented by the ARNL laser localization library, and navigation software program along with mapping software tools and a laser rangefinder

Experimental results . Figure 12: Received signals from a node and a bit close to the transmitter. Figure 11: Initial geometry in a testing environment. The first step in the measurement phase of the received signals is recorded on a laptop, then reading them and signals are stored as files with the extension ".csv" we read, processed and the processing results containing the time stamp, range and velocity information (we don’t need to worry about the time stamp meaning, the only important information is the fact that these are consecutive measurements with an update rate every millisecond). Figure 13: Received signals from a node and a bit far to the transmitter.

The results achieved in the tests of UWB and AmigoBot robot are in Figure 14, 15, 16 and 17 . Figure 14: Test 1- positioning of the mobile robot using Odometry & orientation sensors and UWB from point A to B. Figure 15: Test 2- positioning of the mobile robot using Odometry & orientation sensors and UWB from point A to B. To evaluate the performance of the estimation algorithms of TDOA and the localization algorithm, we chose a mobile node 107 and AmigoBot robot containing thousand positions. For each position we perform ten measurements of the received signal, which has to have thirty TDOA values by position. Figure 16: Test 3- positioning of the mobile robot using Odometry & orientation sensors and UWB from point A to B. Figure 17: Test 4- positioning of the mobile robot using Odometry & orientation sensors and UWB from point A to B and from B to return A.

Table 1 Position of UWB nodes with the estimated error in mm accuracy Connected Node Distance from Node 107 in mm Estimated error in mm 100 15698 55 15205 15000 54 14350 52 12005 50 10025 45 9090 44 8247 36 7074 38 4739 37 3023 35 2900 34 2505 32 2000 1995 24 1884

Advantages and benefits of UWB High bandwidth can support real-time high-definition video streaming Large channel capacity Reliable in hostile environments Delivers higher signal strengths in adverse conditions Offers high performance in noisy environments Low transmit power Provides high degree of security with low probability of detection and intercept High performance in multipath channels Enables ultra-low power, smaller form factor, and better mean time between failures, all at a reduced cost Avoids expensive licensing fees

Conclusions & Future work Experiments have been conducted and investigated using UWB signals measured in LOS and NLOS environments for location estimation using the TDOA algorithm. The measurements were carried out with the UWB radio PulsON 410 and mobile robots (AmigoBot) equipped with an on-board computer, sonar, odometer, encoder, camera and an inertial navigation system. The experimental test showed that the UWB accuracy is very good as the error is between 24 and 55 mm. The result have also shown the maximum error is 55 mm as conducted several times and the long distance the robot travel increase error higher. A new measurement scheme has been proposed for improved localization through performance assessment and validation of the use of UWB signals measured in LOS and NLOS indoor environments. Using a new measurement schemes that improve localization and use this information to perform simultaneous localization and mapping (SLAM). More sophisticated state estimation algorithms such as particle filters using Monte Carlo methods may give more accurate tracking of mobile robots. The performance of UWB in indoor environments was evaluated with the objective of proposing new measurement schemes that improve localization. Algorithms of estimation were simulated and validated by using experimental UWB signals obtained from the PulsOn 410 to localize mobile robots (Amigobots) in indoor environments. Results indicate that a system that uses UWB gives much more accurate position measurement in indoor environments than other localization techniques (which are mostly applied in outdoor environments). UWB provides an accuracy indoors of ±24 mm with a working range of 160 m.

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