**5.1 Gradient estimation**

To verify the algorithm of gradient estimation by three UAS in a circular formation, each platform was placed upside down roughly equiangular around the center

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**Figure 19.**

*center positions.*

**Figure 18.**

*the source direction.*

*Gamma Ray Measurements Using Unmanned Aerial Systems*

of the flight volume as shown in **Figure 18a**. Each UAS was placed at 0.5 m radius mark away from the center. The light source, acting as a radiation source analog, was moved around in a circle concentric with the swarm. The data stream captured through a wireless sensor network was input to the gradient estimation algorithm. Positions of a light source and three UAS were identified by the OptiTrack motion capture system, and used to compute true values of the gradient based on the 1/*R*<sup>2</sup>

**Figure 18b** shows that the gradient direction estimation scheme agrees with the measured data reasonably well. It should be noted that the source distance of 0.7 m was used, which is rather too small for actual contour mapping application in the field. As expected, gradient direction estimation error reached almost 30 degrees which is rather too big for accurate mapping operation. However, it should be noted that experimental measurement data have a good agreement with the computed data.

Due to space constraints of the indoor flight volume, the source-seeking experiment was carried out using multiple Flamewheel platforms to test the gradient estimation algorithm for the heading angle rather than the contour mapping. A light source was placed on a movable dolly within the flight volume, and moved along the *x*-axis. It was shown that the gradient estimation algorithm was effective in determining the direction to the source using the light sensor's data from each UAS.

*(a) Experimental setup for the gradient estimation and (b) plots of gradient estimation errors with respect to* 

*(a) Source-seeking behavior experiment using a light source and 3 lux sensors and (b) plot of source and swarm* 

*DOI: http://dx.doi.org/10.5772/intechopen.82798*

assumption.

**5.2 Source-seeking behavior**

**Figure 17.** *UAS platforms: (a) Crazyflie 2.0 and (b) DJI Flamewheel 450.*

*Gamma Ray Measurements Using Unmanned Aerial Systems DOI: http://dx.doi.org/10.5772/intechopen.82798*

of the flight volume as shown in **Figure 18a**. Each UAS was placed at 0.5 m radius mark away from the center. The light source, acting as a radiation source analog, was moved around in a circle concentric with the swarm. The data stream captured through a wireless sensor network was input to the gradient estimation algorithm. Positions of a light source and three UAS were identified by the OptiTrack motion capture system, and used to compute true values of the gradient based on the 1/*R*<sup>2</sup> assumption.

**Figure 18b** shows that the gradient direction estimation scheme agrees with the measured data reasonably well. It should be noted that the source distance of 0.7 m was used, which is rather too small for actual contour mapping application in the field. As expected, gradient direction estimation error reached almost 30 degrees which is rather too big for accurate mapping operation. However, it should be noted that experimental measurement data have a good agreement with the computed data.

#### **5.2 Source-seeking behavior**

*Use of Gamma Radiation Techniques in Peaceful Applications*

swarm was roughly seven times the speed of the source.

flux distributions were generated (**Figure 15**).

ing robots within the flight volume at 120 Hz rate.

seeking algorithm using a light source of the 1/*R*<sup>2</sup>

shown in **Figure 17a** and **Figure 17b**, respectively.

*UAS platforms: (a) Crazyflie 2.0 and (b) DJI Flamewheel 450.*

**5. Experiments**

**5.1 Gradient estimation**

**4.2 UAS swarm simulation in MCNP computed radiation field**

Mapping this source was possible in this particular case because the speed of the

A realistic gamma-ray flux distributions in a 3D volume with dimensions of 100 m × 100 m × 32 m that contained five photon sources with energy ranging from 3 to 6 MeV were computed using MCNP. A concrete building was used in the model to introduce a structure that attenuates and blocks radiation flux in certain areas of the volume monitored by onboard sensors of UAS. Therefore, complex gamma ray

The reference contour along with performance of the contour mapping algorithm overlaid onto this radiation field is shown in **Figure 16**. A two dimensional radiation distribution at the height of 15 m was used in this simulation (the UAS platforms were moving in the same plane). In this simulation, the swarm's starting position was located inside the radiation field at a point with coordinates (35 m; 35 m). The algorithm allowed mapping the desired contour with a reasonable accuracy.

Key algorithms of contour mapping and source seeking were validated in the indoor flight testbed. This testbed was outfitted with the OptiTrack motion capture technology which allows real-time feedback of the position and orientation of mov-

The Crazyflie 2.0 by Birtcraze and the DJI Flamewheel 450 are two UAS platforms that were used; Crazyflie is an open source, easily modifiable, light weight quadcopter. Its small size enables validation of the contour mapping algorithm with a virtual source. The DJI Flamewheel 450 was used for validation of the source-

was equipped with an onboard omnidirectional light sensor. The Crazyflie and Flamewheel platforms with a single board computer mounted under the frame are

To verify the algorithm of gradient estimation by three UAS in a circular formation, each platform was placed upside down roughly equiangular around the center

type. Each Flamewheel platform

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**Figure 17.**

Due to space constraints of the indoor flight volume, the source-seeking experiment was carried out using multiple Flamewheel platforms to test the gradient estimation algorithm for the heading angle rather than the contour mapping. A light source was placed on a movable dolly within the flight volume, and moved along the *x*-axis. It was shown that the gradient estimation algorithm was effective in determining the direction to the source using the light sensor's data from each UAS.

#### **Figure 18.**

*(a) Experimental setup for the gradient estimation and (b) plots of gradient estimation errors with respect to the source direction.*

#### **Figure 19.**

*(a) Source-seeking behavior experiment using a light source and 3 lux sensors and (b) plot of source and swarm center positions.*

**Figure 20.**

*(a) Contour mapping experiment with the swarm of three Crazyflie UAS (circled in green) and a virtual source (circled in red) along with a real-time data display window and (b) trajectory of the swarm's center.*

The swarm was restricted to move along the *x*-axis and the reference position generation was bound to ±0.5 m, in order to minimize risk of the swarm crashing into the walls of the flight volume. The tracking of the light source and three UAS platforms was carried out using the OptiTrack. Note that an embedded window to display source-seeking performance in real time using the motion capture system in the flight volume is shown in **Figure 19a**. **Figure 19b** shows how the UAS swarm's center moves as the source is moved. As expected, the oscillatory motion of the swarm's center occurs due to gradient estimation errors using a finite number of light sensors.

#### **5.3 Contour mapping behavior**

To demonstrate the effectiveness of the contour mapping algorithm in the indoor flight volume, three Crazyflie platforms were used. A virtual source was used due to a limited payload and communication capability of the Crazyflie UAS. As shown in **Figure 20a**, a virtual source was located on the ground and the OptitTrack tracked the source and each UAS in the swarm. The 'source strength' needed for the gradient estimation algorithm was calculated using the 1/*R*<sup>2</sup> model where *R* was obtained from the virtual source position data from each UAS. **Figure 20b** shows the experimental results on a plot of motion of the swarm's center following the reference contour defined with respect to the virtual source located on the floor. It maps the reference contour within ±0.1 m, which is less than 8% of the size of the contour.

## **6. Conclusion**

Ambient temperature CZT and CLYC sensors were integrated onto the UAS platform using the plug-and-play approach. The CZT sensor was designed for high resolution gamma spectroscopy. The CLYC sensor enables gamma and neutron measurements with an excellent neutron-gamma pulse shape discrimination with the figure of merit 2.3. The automated spectral analysis code locating peaks and calculating their intensities was developed for both sensors.

USB hardware connections were used to link the sensors and the main controller with the UAS power source. ROS was used for the data communication and data fusion. To streamline the process of bridging disparate components into a cohesive network, the collection of libraries describing the publisher/subscriber

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provided the original work is properly cited.

*Gamma Ray Measurements Using Unmanned Aerial Systems*

communication of ROS nodes was developed for these sensors. The sensor's design

The method of contour mapping and source seeking in the radiation field by the UAS swarm equipped with gamma-ray detectors was developed. The method is used for low altitude applications where fixed wing UAS platforms are not suitable. The source-seeking and contour mapping algorithms were validated using a realistic radiation field with scattering and attenuation of gamma flux computed with MCNP code. The algorithm was implemented to map the radiation contours for multiple radiation sources and also a moving source. Moreover, it showed an effective way of cutting down the flight trajectories of three flying platforms by adaptively updating a swarm's spin rate based on averaging the measured radiation data. The UAS swarm enables surveying an unknown, contaminated environment and mapping the area while locating the radiation sources, thus helping first responders to enhance situational awareness and to manage operations and safe-

This work was supported by the Department of Energy Minority Serving Institution Partnership (MSIPP) managed by the Savannah River National

supports hot swapping and does not require restarting the system.

*DOI: http://dx.doi.org/10.5772/intechopen.82798*

guard personnel.

**Acknowledgements**

**Conflict of interest**

**Author details**

Laboratory under SRNS contract MSIT00016.

The authors declare no conflicts of interest.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Monia Kazemeini, John Vargas, Alexander Barzilov\* and Woosoon Yim

University of Nevada—Las Vegas, Las Vegas, NV, Unites States

\*Address all correspondence to: alexander.barzilov@unlv.edu

#### *Gamma Ray Measurements Using Unmanned Aerial Systems DOI: http://dx.doi.org/10.5772/intechopen.82798*

communication of ROS nodes was developed for these sensors. The sensor's design supports hot swapping and does not require restarting the system.

The method of contour mapping and source seeking in the radiation field by the UAS swarm equipped with gamma-ray detectors was developed. The method is used for low altitude applications where fixed wing UAS platforms are not suitable. The source-seeking and contour mapping algorithms were validated using a realistic radiation field with scattering and attenuation of gamma flux computed with MCNP code. The algorithm was implemented to map the radiation contours for multiple radiation sources and also a moving source. Moreover, it showed an effective way of cutting down the flight trajectories of three flying platforms by adaptively updating a swarm's spin rate based on averaging the measured radiation data. The UAS swarm enables surveying an unknown, contaminated environment and mapping the area while locating the radiation sources, thus helping first responders to enhance situational awareness and to manage operations and safeguard personnel.
