Radioactive hot-spot detection using unmanned aerial vehicle

surveillance

Duarte Felix Macedoduarte.macedo@ist.utl.pt

Instituto Superior Tecnico, Lisboa, Portugal

November 2018

Abstract

This work compares different approaches to estimate the position, activity and the number of sourcesof radiation and proposes the best solution. The compared approaches use the measurements of ra-dioactive levels acquired in different positions to estimate the location of the radioactive sources, as wellas the number of sources and their respective activity. The approaches were tested using a computersimulation and, then, in a laboratory environment. The integration of a collimator was also tested. Theapproaches are the MLA (Maximum Likelihood Approach), the RLCA (Robust Location with Cluster-ing Approach) and the LSEA (Least Square Error Approach). They were tested in 2D and 3D, and in2D with collimator. The MLA provides the best results using computer simulation. The used sensorwas a commercial GMT (Geiger-Muller tube) and the collimator was designed and implemented bythe author. The experiments performed in 2D with and without the collimator provided better resultswhen compared to 3D. Additional experiments were performed with a UAV (Unmanned Aerial Vehicle)in controlled outdoor environment with sources of radiation, but without the collimator. The GMC(Geiger-Muller counter) was installed in the UAV equipped with a GPS (Global Positioning System)receiver. The experimental results are reliable in order to proceed with the work with other radiologicalsensors and using a fleet of UAVs.Keywords: UAV, Radioactive Source, Radioactivity, Detection, Collimator

1. Introduction

Radioactivity is a physical phenomenon that occursnaturally when an unstable isotope element decayto another element and emit ionizing radiation. Insome areas with abnormal levels of radioactivity,it is important to know the places with high lev-els of radioactivity to prevent the future risks ofpublic health or radioactive contamination. Highlevels of radioactivity can be dangerous for publichealth. On one hand it may damage the cells be-cause the radiation energy may destroy parts of thecell or its function . On the other hand, the highlevels of radioactivity can indicate the presence of aradioactive mineral. These minerals are importantfor nuclear industry.

One way to analyse, to monitor or to study theareas is making a map of the radioactivity. Thisidea consists of a map that represents the locationof hot-spot radioactive sources. The idea of thework is to make this with UAV to operate in alltypes of terrain, it is low cost and easy to use.

1.1. Hot-spots detection approaches and applica-tion with robots

There are previous works describing approacheswhich identify the parameters and number ofsources.

Maximum likelihood approach (MLA) is based onthe calculation of the parameter in the maximumvalue of the likelihood. The likelihood is calculatedwith a Poisson distribution where the samplings arethe level of radioactivity and the parameters are theposition and intensity of sources in the model of thedistribution of radiation. In the work described in[1] it is used this approach for the simple case ofa single source. In the work described in [2] it isused this approach to estimate the parameters ofan unknown number of sources.

The work described in [3] uses a ratio of squaredistance to discover possible positions of a low-levelradioactive source with two pairs of a group of threeand intercept the solutions. The results have twopositions. So the process is repeated for every com-bination of three possible s. The work uses the clus-tering process to find the area with more concentra-tion of possible position. The final source chosen isthe source with with more signal noise ratio. In the

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end, to discover the intensity, the equation of modelof radiation distribution is calculated.

In the work [2] it is described the Bayesian ap-proach that chooses randomly particles. Each onerepresents the characteristic of a number of sources,for each one it is calculated the probability withthe s information. In each iteration, particles arere-sampled according to weight. Weight is propor-tional to the probability. Probability is calculatedwith likelihood function with adaptive step factor.In each iteration the particles are actualized ac-cording to a kernel density function. When iter-ations finish, the estimated sources are the aver-aged of particles. The process is repeated for differ-ent numbers of sources and the chosen case is thecase with more probability (likelihood plus poste-rior function).

In the works described in [4] and [5] robust lo-calization with clustering approach (RLCA) is usedto estimate the number, activities, and positionsof sources. The approach chooses initial randomlyparticles. Particles represent sources with positionand activity. In each iteration, particles are classi-fied by weight. The weight is proportional to likeli-hood. The worst particles are replaced by the par-ticles with the closest parameters of those of thebest particles and few numbers with totally randomparticles. It is also used a clustering process in par-ticles. The clustering process estimates the sourceswith the centroids. The iterations finish when theconfidence score is bigger than the stipulated value.

In the work described in [6], the least square er-ror approach (LSEA) is used to discover the posi-tion of a single source. In this work, 4 sensors areused. The approach applies least square error tothe model and s, however, in this case, it discoversthe temporal change of background and position.

The aprouch used in article [7] is used to discoverthe activity and position of one source. This ap-proach fits a function of a wave in data. The firststep is to remove the background and noise with amedium filter. Next, it is made wavelet transformwith Mexican hat wavelet. With the obtained func-tion it is calculated the position and the activity ofsource.

In work [8], it is used a multi-scale support vectorregression with a kernel function of Gaussian radialbasis function to divide the area in areas with dif-ferent levels of radioactivity. The final result is aheatmap where is possible to see the location of thehot-spots.

Some works use UAV and UGV (UnmannedGround Vehicle) controlled by tele-operation or au-tonomy to make the of radioactivity, because of thedangers of radioactivity in health and the difficul-ties to make s on the field.

In the work described in [9] and [10] a UAV with a

gamma-ray spectrometer, a GPS (Global Position-ing System) receiver to measure the position anda range finder sensor to measure the distance untilthe ground are used. The information is the po-sition and measured intensity. UAV is always inmovement and acquires with a frequency of 2 Hz.The data is used to build a 2D heatmap with ra-dioactivity information. In the work described in[11] the same instruments in UAV are used to makean elevation heatmap with the data. In the work[12] a cheaper instrument is used, the UAV withGMC is used to build a 2D heatmap. In works [13]and [14], it is used a fleet of UAVs. It is studied theflight in formation of a fleet of UAVs. The studyshows that the fleet has results with more reliableinformation than a single one. This is used to de-tect nuclear radiation. This can be used to map anarea faster.

There are works that use a UGV to discover theposition of hotspots or mapping, these vehicles havethe advantage that, on the ground, the radioactivelevel is bigger than the level measured by the UAV.In work [5], a UGV is used for the s and use RLCAto discover the position of the radioactive source.In works [15], [16] and [17], a cooperation of anUAV and an UGV is used to discover sources orto build 2D radioactive heatmap. The UAV buildsthe radioactive heatmap of a big area and UGVcarries out a more detailed analysis on the groundwhere the radioactive levels are higher. They usescintillation detectors.

Some works carries out a radioactive inspectionin indoor environment like in a building. In work[18] is used a video camera and a depth camerato have a 3D structure of the indoor scenery andthe localization of UAV, and a GMC to measurethe radioactive level. With this data 2D heatmapof radioactivity is created. In works [19] the samesensors of the previous work are used in a 3D re-construction of the indoor environment as well asan indoor environment 3D radioactive heatmap.

There are some sensors measured the radioactivelevels that are portable and can be transported bythe UAV. The chosen radiative sensor is GMT be-cause it has a very simple use, it is cheap, anda small size, which makes it ideal for transporta-tion by UAV. This sensor is part of a detectorcalled GMC. The GMC does not measure radio-logical magnitude, but it can be used to estimatemagnitudes in doses or levels of radioactivity [20].

Correctly, other works find number, position, andactivity of some sources. Other works use mobilerobots to record the measurement of radioactivelevels with a gamma-ray spectrometer in order tocreate a radioactivity map or to identify radioac-tive sources.

The propose of the thesis is to look in to the ap-

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proaches of other works and adapt them, and usinga UAV with GMC to find the number, positions,and activities of a group of radioactive sources. Tocarry this out, some measurement with different po-sitions have been made. Other works did not usea collimator, so another objective is to project acollimator together with a GMT and to verify theviability of collimator use.

The model of radioactive intensity propagationis a model where the radioactivity decreases withthe inversion of square distance towards the sources,where the radioactivity levels represent an avaregelevel of radioactivity [20]. This is the model usedby the majority of works. It is considered that thesource activity is usually considered stable. Eachhot-spot in the work will be dealt with as a singlepoint so in the text it will be referred to as a source.In this work, the approach is defined as a techniqueused to resolve a problem and process is defined asa group of actions used to reach an objective.

2. Approaches used to identify hot-spotsThe approaches chosen are the MLA (multiplesources), RLCA and LSEA. The reasons for choos-ing MLA are: it is simple to put in to practice, itwas used to find the number and the characteristicsof the sources, it is simple to adapt to the collima-tor model and it also works with fewer measure-ments compared to other approaches. The reasonsfor choosing RLCA are: it was tested in 3D simula-tions, RLCA does not repeat a process with differ-ent number of sources, and is also simple to adaptto the collimator model. The reasons for choosingLSEA are: it shows good results with one sourcehaving few measurements and is simple to adapt tomultiple sources. It has the advantage of workingwith much fewer measurements compared to otherapproaches and it is simple to adapt to the collima-tor model.

To minimize the function of likelihood in MLAand error function in LSEA the Poll process is used.This process using pre-determined function on mat-lab.

2.1. Maximum LikelihoodA number of sources are acknowledged in the be-ginning. Due to computational reasons in sourcesidentifying the approach minimizes the equation ofminus logarithm of likelihood function.

v(s) =

m∏j=1

∑Nl=1 f(sl, qoj )coj e−

∑Nl=1 f(sl,qoj )

coj !(1)

f(sl, qoj ) is the estimated radioactivity measuredin position qoj relative to the estimated source sl.

s = mins1,s2,...,sN

(− log(v)) (2)

When the estimated sources are too close to themeasurement, this is not considered in eq. 2.

This process is repeated with different numbersof sources. The chosen case is the one that has thehighest result of the function 3.

βs = log (p (s))− 1

2log |J (s)| (3)

Where J is the Fisher information.When the sources are too close they are said to

have the same average position and the sum of ac-tivities.

2.2. Robust Localization with ClusteringThe approach starts with some particles from ran-domly chosen sources parameters . Initial weightsof particles are the same.

The weight is calculated with the likelihood func-tion that is a Poisson distribution of the propa-gation model of radiation with the measurements,multiplied by previous weight.

w(θk,t, k) = w(θk,t−1)v(θk,t) (4)

9 per cent of the sources with the worst weightare changed with a particle randomly chosen by thehalf best sources and one per cent of other worstweight is allocated with another random positionand activity. This process is repeated until final-ization after a defined number of sources and theclustering process is made at the end.

Clustering process k-means is used to find thenumber of sources and divide the particles in differ-ent sources. The source parameter is obtained bymeans of source groups averaged.

2.3. Least Square ErrorThe approach used in article [6] uses the positionand intensity of radioactive levels of some findingsto discover the background and position of one ra-dioactive source.

This process calculates the least square errors ofthe model of radiation propagation(eq. 5).

e =

m∑j=1

(coj −

N∑l=1

f(sl, qoj )

)(5)

This last process is repeated with a different num-ber of sources. The number of sources chosen is thecase with a minimum value of eq. 5.

2.4. Application of CollimatorWhen the collimator is used, it is considered in themodel of propagation of radioactive radiation, thatwhen the source is not inside the solid angle of thesensor, it is not considered in radioactivity field bya sensor or is attenuated. It is considered a perfectcollimator that is symmetric. So, the solid angleconsidered is symmetric with a form of a cone as

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solid angle Ω in fig. 1(a). In approaches, to knowif the source is inside the solid angle, the criterionis when at the angle with vertical and source (angleis represented in fig. 1(b) as ω) is smaller than thestipulated care considered inside of the solid angle.

(a) Solidangle

(b) Angle with vertical and source (theblue source isn’t measured or attenuatedand the orange source is measured).

Figure 1: Collimator solid angle

3. Implementation and Results3.1. Results Obtained by Simulation

It was tested to search sources in 2D and 3D. Inboth tests, the objective is to discover the numberand characteristics of sources. In the 2D tests, thesources were distributed in an area of a 100*100cm2 and recordings are made in grid dispositionor random disposition. The number of recordings(m) is the same in random and in grid. In 3Dtest the sources were distributed in a volume of a100*100*50 cm3 and measurements were made in3 plans in 50 cm, 60 cm and 70 cm heights withdistribution in grid.The sources are randomly dis-tributed in square area and activity is randomlychosen between 10 and 30.

Firstly, it was done with three chosen approaches.The 2D tests were made with the recordings atground level and 5 cm high. The test was madewith 1, 2, ..., 10 sources, with 441 recordings for 2Dtest and repeated several times and 441*3 record-ings for the 3D tests. The tests compare the realnumber of sources with the estimated sources.

MLA and LSEA have acceptable results in the 2Dtests at ground level (fig. 4), but RLCA has badresults mainly for a big number of sources. Afteran analysis of the RLCA results, the big problem isthat the more active sources attract the more parti-cles and the approach have difficulty to distinguishclosed sources. The RLCA was discarded for nexttests.

The 2D tests at 5 cm high (fig. 3) have a goodresult, very similar to the previous with a smallimprovement in tests with random recordings. Asin previous tests, the MLA has little better resultsthan LSEA but they are not significant.

In the 3D test, the results are in fig. 2. It ispossible to see that the error is bigger and in theLSEA tests, the tests with more sources have a bad

Figure 2: Results of 3D (first column MLA andsecond column LSEA)

Figure 3: Results of 2D test with measurement at 5cm high (first line measurements in grid and secondline random measurements, first column MLA andsecond column LSEA)

prediction of sources. After these tests, it was de-cided to discard the LSEA because MLA has betterresults in all previous tests.

Figure 4: Results of 2D test with measurement ata level of the ground (first line measurements ingrid and second line random measurements, firstcolumn MLA, second column RLCA and third col-umn LSEA)

Noise started to be considered in order for record-

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ings to be more realistic. The noise in intensityof radioactive level is simulated adding a Gaussiannoise number to the intensity parameter. The noiseposition is simulated through different recording po-sition compared to the initial position. The record-ing is made in the initial position added with Gaus-sian noise. The tests are made with noise in inten-sity, noise in position and in both. The tests weremade using 1 to 5 sources, estimated approach 1to 8 sources, and for 441 and 121 measurement 2Dtests with measurements at 5 cm high. Noise is rel-ative to intensity average measured and study areaside.

The 2D test simulation shows that the tests withrandom recordings are more affected with noise andwith 441 recording the results start to be non-acceptable at 20% noise (fig. 5(a)) and at 121recordings at 10% noise (fig. 5(b)). The resultsshow that the approach is more affected by noise inintensity.

In 3D test was expected to shows more sensibilityto noise in intensity and less to the noise in positionmeasurement. The tests are unacceptable from 5%noise level (fig. 5(c)) and higher. It is possible tosee that the 3D test is more sensitive to the noise.

In this part a measurement simulation was car-ried out using a collimator to understand its effectin tests with and without noise. The tests were donewith different collimator opening (solid angle) tocompare with and without collimator with the con-ditions of the 2D tests at 5 cm in height. The cho-sen angles are π/6 and π/12 rad. Simulation showsthat the collimator result improves when noise is ashigh as 20% (fig. 6 and fig. 7) in the measurementsposition.

When it has noise in intensity or both intensityand position there is an improvement in angle π/6rad but it is not significant. The situation withoutnoise has the same result.

The following tests, compare the real position andactivity with estimated position and activity by ap-proaches. These tests are done with just one sourceand in both cases average and variance of devia-tion is resisted as well as activity and position, intests that were repeated 500 times. The tests werecarried out in the same conditions of noise as inprevious tests (without collimator, with collimatorwith angle of π/6 rad and π/12 rad).

The simulation with 441 measurements (fig. 8(a)and 8(b)) had better results. The tests without col-limator had better results with noise in intensity.The collimator only resulted in an improvement ofresults in the sources position using noise in mea-surement position, in other greater error was reg-istered, resulting in worse activity estimation whilethe collimator.

The same test was with 3D tests. The results are

(a) Results with 10% error and 121 measurements

(b) Results with 20% error and 441 measurements

(c) Results with 5% error

Figure 5: In images, the first line is built with mea-surements in grid and in the second line randommeasurements, the first column, counting from leftto right, does not have noise, the second column hasnoise in intensity measurements, the third columnhas noise in position measurement and the fourthcolumn has noise in both.

shown in fig. 9(a) and 9(b). The results show thatthe approach is less sensitive to noise in position.

In general speaking, the random measurement re-sults had worse results than measurements in grid.

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Figure 6: Results with collimator angle π/12 rad,with 20% error and 441 measurements (as in fig. 5)

Figure 7: Results with collimator angle π/6 rad,with 20% error and 441 measurements (as in fig. 5)

3.2. Experimental Results

In this section, the approach that is used to identifysources is the same approach as in previous section,MLA.

The available radioactive sources in the lab aretwo, one of 40-Potassium (40K) with about 1000Bq and 241-Americium (241Am) with unknown ra-dioactive intensity. The measurements have 20 sec-onds to get results in order to show less error in in-tensity. The source does not have uniform radiationlevels in all directions. The GMC used is a Spark-Fun Geiger Counter - SEN-11345 with a GMT LND712.

The GMC was studied. It has the same responsein all directions. The conversion factor was calcu-lated to convert the counts measured by the GMCof radioactivity levels in the model. The collima-tor was designed (fig. 10) with a sheet of lead of3 mm. The collimator consisted of two parts. A 2

(a) Results of uncertainty of activity of source and 441 mea-surements

(b) Results of uncertainty of position of source and 441 mea-surements

Figure 8: In images show the first line was built us-ing random measurements and the second line us-ing measurements in grid, the first column, count-ing from left to right, has noise in intensity mea-surements, the second column has noise in positionmeasurements and the third column has noise inboth. In each graphic, blue lines are without colli-mator, red lines are with angle π/6 rad and yellowlines are with angle π/12 rad.

mm radius hole was built to enable the radiationpass. The collimator was design to weight less than350 g due to UAV payload.

After a study was carried out, the collimator an-gle was found. The distance of the source to theGMT was stipulated and measured in different di-rections. The result of collimator angle is π/16 rad.

The first tests are to identify sources in 2D areas.In these tests the sources are put on the ground

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(a) Results uncertainty in position in 3D tests

(b) Results uncertainty in activity in 3D tests

Figure 9: In the first column, the image show, fromleft to right, noise in position measurements, thesecond column has noise in intensity measurements,the third column has noise in both.

Figure 10: (left) Collimator design, and (right)Parts of collimator

in an area of 100*100 cm2 and the measurementsis made in a grid of 36 recordings positioned in 15and 30 cm in height. In fig.11(a) it is possible tosee the measurement and position sources of 15 cmhigh test, as well as the test with 30 cm in height(fig.11(b)). The real sources are in positions [30 75]and [45 20] cm in both tests.

The tests detect the number of sources but witha small error of in average 6.6 cm in positions andbig error in activity because the source of americiumis not isotropic and the source of potassium is lessactive.

The second tests are to identify the source in 2Darea of 100 ∗ 100 cm2 with and without collimatorto compare the information. It was done with justone source of americium with measurements at 45.5cm in height with and without collimator to com-pare them. The tests were done with 121 and 36measurements.

In the collimator test with 121 measurements, theerror in position was 3.1 cm. In the test withoutcollimator with 121 measurements, the error in po-sition was 23.3 cm. In the collimator test with 36

(a) 36 measurements, 15cm high

(b) 34 measurements, 30cm high

Figure 11: measurements and source position esti-mated (O) and real (X)

(a) Experimental resultwith collimator estimated(34.6 68.1) cm and 9477Bq

(b) Experimental resultwithout collimator esti-mated (14.4 75.8) cm and28228 Bq

Figure 12: Experimental result with and withoutcollimator, 121 measurements, source [35 65] cm

(a) Experimental resultwith collimator estimated(71.0 38.0) cm and 10666Bq

(b) Experimental resultwithout collimator esti-mated (91.4 26.2) cm and27795 Bq

Figure 13: Experimental result with and withoutcollimator, 36 measurements, source position [80 35]cm

measurements, the error in position was 9.5 cm. Inthe test without collimator with 36 measurements,the error in position was 14.4 cm. The collima-tor improves in position and gets worse in activity.However there is a problem, in the real world, thecollimator attenuates the activity of sources becausethe GMT is not a point and collimator reduces thearea of the sensor. If it is used with UAV, moretime of flight is necessary using more weight, thushaving more energy consumption.

A 3D test was done to identify the source ina volume. Sources were located in a volume of100*100*40 cm3 and the measurements are madeat 50, 60 and 70 cm in grid with 36 measurements

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in each level, 108 in total.The estimated number of sources was wrong. As

can be seen in figure 14 The 2D position, in x-axesand y-axes, of estimated sources, are close to therespective source and in height which is inaccuratein z-axes. One problem with the test is that one ofthe used sources in the lab is much more active thanthe other. As in simulations test this test shows that3D searching is very sensitive to the noise.

Figure 14: 3D test with sources in [25 45 7] and [7580 29.5] cm and estimated source is in [29.7 52.140.0] cm with estimated activity of 13139 Bq

In this part, the measurement was done withGMC attached to UAV Quadcopter Dji Phantommodel (fig. 15(a)). The duration of measurementslasts only 3 seconds because the UAV is in constantmovement during the flight. The measurement pa-rameters are registered with the help of app MaRIAon a smartphone. The measurement activity is thenumber of counts every 3 seconds of the GMC andthe position is measured with the GPS receiver ofthe smartphone.

The tests are made in the area with two sourceson the ground (fig. 15(b)).

(a) Assembly UAV withGMC

(b) Test location: X is the locationof sources ((27.9 13.9) and (25.47.2) m) and circle the consideredreferential origin

Figure 15: Assembly of UAV with GMC and testlocation

Experimental tests were performed by a personcarrying the GMC while walking on the study areaand then using the GMC installed on a UAV whileflying over the study area.

There was a problem, the measurements weredone in a short time span and the position of theGPS receiver of the smartphone had a big error,mainly in height. So the measurements in heightwith disperse values among each other were re-moved. One option is to use measurements withoutpre-approaches treatment and the other is to builda measurement grid with the measurement averagein squares of 1 m2 area. The last option is statisti-cally the one with more valid results.

The results, in first flight, estimated source withposition [31.5 13.5] m and activity 14290 Bq, sec-ond flight estimated source with position [32.7 15.3]m and activity 49027 Bq, test on foot estimatedsources with position [26.2 22.0] and [23.1 11.0] mand activity 31824 and 35336 Bq (figures 16(a),16(b) and 16(c) respectively).

(a) First test with UAV

(b) Second test with UAV

(c) Test by foot

Figure 16: Measurements with handling andsources (X) and estimted sources (O)

In spite of error in data position, with these testsit is not possible to verify if the handled measure-ments show good results in position. But it in terms

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of activity, the handled measurement tests show re-sults closer to those of lab test. Therefore, bettermeasurement distribution and measurement posi-tion are required.

4. Conclusions

The radioactivity monitoring is necessary to pre-vent health problems, to prevent terrorist attacksand to discover radioactive mineral with commer-cial value. The objective of the work is to iden-tify the parameters of radioactive sources in an areawith a UAV. This thesis proposed a solution to es-timate the localization, activity and number of ra-dioactive sources. The solution used a radiologicalsensor to record measurements within the studiedarea.

The GMT and other radiological sensors areportable and could be carried by a human, however,the UAV can carry these sensors and can be usedto make an exhaustive carry-out a complete cov-erage of the area, with a previously detailed routeor maybe using a fleet of UAV, and in all types ofterrain.

In this issue, the focus of error is in the positionand activity measurement of recorded radioactivelevel.

Some approaches were tested, MLA, RLCA andLSEA. In the computational tests that identify ra-dioactive source numbers, the MLA had better re-sults than other approaches in the 3D tests and 2Dtests. So the MLA was the chosen approach. Thesetests that identify the number of sources are a goodcriterion because a big scale test can have a lot ofsources. The experimental test in the lab to iden-tify the number was not conclusive because it wasjust tested with two sources and one of these wasnot isotropic.

With the introduction of noise in measurements,the number of sources detection test showed thatnoise in the intensity of measurements had worseresults than noise in the position of measurementsand more measurements had better results. It ispossible to conclude that errors in measured inten-sity lead to more error in the final result than errorsin the position of measurements. These tests showthat it is equally important to make longer measure-ments as well as measurements in big quantities.

The simulation tests with collimator showed im-provements in the results with noise in position,and had significantly worse results with noise inintensity measurement in the prediction of sourcenumber and uncertainty of activity source predic-tion with noise, but it showed better results in posi-tion uncertainty of predicted source with noise. Theexperimental results in the lab show that collima-tor reduces the number of measurement counts, butimproves the predicted position, even in lab cases

inwhich sources were not isotropic. The use of colli-mator with UAV and GMC was not viable becauseit implicated more flying time in order to achieve agood number of counts and had worse results in theidentification of activity source.

Computational 3D tests had an acceptable resultwithout noise and with noise in measurement posi-tion. The experimental 3D test showed a very badresult. In the 3D test, much more measurementprecision was needed than in the case of 2D test.

In a field test with UAV to resolve the problemof the UAV not being on a fixed location, shortperiod measurement were made to improve the in-tensity measurement and an average grid was cre-ated in order to obtain better results. The re-sults in a controlled scenery revealed that it waspossible to identify a hot-spot of radioactivity butan evenly distributed measurement was needed toprevent artefact source and improve located sourcemeasurement

The solution put forward was a UAV with a GMCand mobile phone to register the data with 3 sec-ond measurements using a grid that records averagelevels of local measurements.

Concluding, the MLA was adapted for to bringtogether close sources and also for the use of a col-limator, which showed good results.

The computational results show that has impor-tant to have big measured data as well as long timemeasurements. After a study done in the lab as wellas computational, it is possible to conclude that itwas not viable to use a collimator during the UAVflight because more flight time was needed moretime of flight and due to collimator mass the au-tonomy decreased and the collimator reduced thenumber of counts.

A low-cost solution was made to measure andrecord measurement with a GMC and a mobilephone that could be used with a UAV as well aswith a person carrying it. The final objective, whichwas to identify sources on the field with UAV, wasaccomplished.

4.1. Future Work

While carrying out this work, some problems cameup such as lack of time and equipment. The follow-ing are some things to do in the future:

Using a sensor that enables a gamma-ray spec-trogram to identify radioactive isotopes.

Using a bigger UAV with greater autonomy andGMC collimated.

Obtaining measurements using a fleet of UAVs.

Obtaining evenly distributed measurements in ra-dioactive leak and mineral location using UAV.

Related experimental part with isotropic sourcesand with known activity, better equipment for posi-tion measurement as well as higher registers of big

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quantities of measurements.

Using a range finder sensor or 3D scanner to ob-tain a precise altitude of the UAV measurementsand enable the search in an area with elevation.

Testing the approach in the case where the sourceis considered a physical volume to identify the dis-tribution of the radioactivity and the source form.

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