Towards Autonomous Mobile Inspection Robots Using Edge AI

Ricardo C. C

amara de M. Santos

, Mateus Coelho Silva

, Rodrigo Lucas Santos

Emerson Klippel

and Ricardo A. R. Oliveira

Departmento de Computac¸

ao - DECOM, Universidade Federal de Ouro Preto - UFOP, Ouro Preto, Brazil

Keywords:

Autonomous Mobile Robot, Inspection Robot, Robot, Edge AI, Artiﬁcial Intelligence, Deep Learning, CNN,

YOLOv7, Feedback Control, Object Detection, Jetson Xavier NX.

Abstract:

Recent technological advances have made possible what we call industry 4.0 in which the industrial environ-

ment is increasingly ﬁlled with advanced technologies such as artiﬁcial intelligence and robotics. Defective

products increase the cost of production and in such a dynamic environment manual methods of equipment

inspection have low efﬁciency. In this work we present a robot that can be applied in this scenario performing

tasks that require automatic displacement to speciﬁc points of the industrial plant. In this robot we use the

concept of Edge AI using artiﬁcial intelligence in a edge computing device. To perform its locomotion the

robot uses computer vision with the brand new YOLOv7 CNN and feedback control. As hardware this robot

uses a Jetson Xavier NX, Raspberry Pi 4, a camera and a LIDAR. We also performed a complete performance

analysis of the object detection method measuring FPS, consumption of CPU, GPU and RAM.

1 INTRODUCTION

The technological advances of the 21st century made

possible the fourth industrial revolution, also known

as industry 4.0. Industry 4.0 is the combination

of several technologies, such as digital technologies

(e.g., artiﬁcial intelligence, Internet of Things, and

blockchain) and other technological advances (e.g.,

robotics, digital twins, and cyber-physical systems)

all applied to an industrial production environment

(Hassoun et al., 2022). In this way, the industrial

environment is moving towards having a high-level

collaboration between robots and human workers to

increase safety, ﬂexibility, and productivity.

Defective products increase costs and deteriorate

manufacturing processes. Inspection routines for the

preventive detection of anomalies and problems are

of paramount importance for efﬁciently maintaining

an industrial plant. Conventional manual inspection

methods have a high workload, offer risks and have

low efﬁciency. In addition, they cannot continuously

satisfy the increasing quality standards of the means

of production. Therefore, robots helps to reduce these

https://orcid.org/0000-0002-2058-6163

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https://orcid.org/0000-0001-5167-1523

problems and it is one of the research focuses around

the world (Ebayyeh and Mousavi, 2020).

A fundamental component of Industry 4.0 is the

ﬂexible manufacturing system, an advanced pro-

duction system that interconnects machines, work-

stations, and logistical equipment, with the entire

manufacturing process. This fabrication system is

intended for highly complex manufacturing tasks

of great topological diversity, guaranteeing delivery

times and minimum manufacturing costs and with

frequent changes (Florescu and Barabas, 2020). In

this way, the need for autonomy in the movement of

the automatic inspection agent arises. So the inspec-

tion agents should move to the inspection sites au-

tonomously and appropriately.

For this matter, we present an autonomous loco-

motion method based on Edge AI technology. This

work aims to present a robot capable of moving to

speciﬁc points of the production plant. These sensors

are a camera and a LIDAR. The robot moves to tar-

get positions using object detection in images through

the usage of a CNN (Albawi et al., 2017a) applied to

images captured from a camera installed on the robot.

LIDAR is used to measure the robot’s distance to the

target position and its arrival at the required positions.

Santos, R., Silva, M., Santos, R., Klippel, E. and Oliveira, R.

Towards Autonomous Mobile Inspection Robots Using Edge AI.

DOI: 10.5220/0011972200003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 1, pages 555-562

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

555

2 THEORETICAL REFERENCES

This section presents the necessary theoretical frame-

work for creating the robot and its autonomous loco-

motion methodology.

2.1 Autonomous Mobile Robots

An autonomous mobile robot (AMR) is a system

designed for operation and navigation in an unpre-

dictable and partially unknown environment. For

this, the robot must navigate without interruption and

avoid collision with obstacles within a known con-

ﬁned environment (Ishikawa, 1991). The AMR re-

quires little or no human intervention to navigate and

move around and is designed to follow a pre-deﬁned

path, whether indoors or outdoors.

The fundamentals of mobile robotics consist of

locomotion, perception, and navigation (Alatise and

Hancke, 2020). Indoors, the mobile robot usually re-

lies on the ﬂoor plan, sonar location, and inertial mea-

surement unit (IMU), among other sensors. For the

robot to function, it needs to have a range of sensors

that offer an internal representation of the environ-

ment.

2.2 Convolutional Neural Networks

Machine Learning is a ﬁeld of research in the sub-

ﬁeld of Artiﬁcial Intelligence (AI) that studies the de-

velopment of methods capable of extracting concepts

from data samples. One of the methods used for this

purpose is artiﬁcial neural networks (ANN) that can

be understood as a non-linear mathematical model to

predict or generate content based on input data. Net-

works are formed by layers of neurons, which are

the basic units of networks. Each layer of neurons

is connected to each other through connections called

synapses which are the output signal of each neuron

and, at the same time, the input signal of the next

layer, so the information propagates through the net-

work. These synapses are associated with weights de-

ﬁned during the training of the network so that the

model learns to process the information appropriately

(Krogh, 2008).

The term Deep Learning refers to ANN with

a considerable amount of layers. Interest in hav-

ing deeper hidden layers has outperformed classical

methods in different application areas, especially in

(Albawi et al., 2017b) pattern recognition. One of

the most popular deep neural networks is the Con-

volutional Neural Network (CNN). They take this

name because it is the name of the linear mathemat-

ical operation between matrices called convolution.

CNNs perform very well on machine learning prob-

lems. They have several layers that identify hierarchi-

cal patterns. These layers include convolutional layer

learning ﬁlters that run on the input image, forming

a feature map as output. Pooling layers are used to

subtract information from the previous layer. Upsam-

pling layers can be used to augment information. Ap-

plications dealing with image data are widely used for

object detection (Arulprakash and Aruldoss, 2022).

2.3 Edge AI

Current artiﬁcial intelligence applications, such as

deep learning, are techniques that require high com-

putational power for their execution, which creates

a technical challenge to adapt artiﬁcial intelligence

applications for embedded devices (Li et al., 2019).

Edge AI is a new perspective that beneﬁts from Edge

Computing concepts to develop artiﬁcial intelligence

applications.

Edge computing and artiﬁcial intelligence, by na-

ture, have conﬂicting requirements. AI generally re-

quires more processing, while edge computing builds

primarily on hardware miniaturization and increased

mobility. However, a recent trend has shown an

increasing number of applications applying the two

concepts together, especially in mobile edge comput-

ing (Chen and Ran, 2019). In this way, the Edge

AI (Wang et al., 2019) concept has the challenge of

adapting AI and hardware models to make it possible

to develop applications for this context.

3 RELATED WORKS

In Szrek et al. (Szrek et al., 2022), a mobile inspec-

tion platform based on autonomous UGV (Unmanned

Land Vehicles) was proposed, equipped with various

sensors (RGB image, sound, gas sensor, among oth-

ers). They used a laboratory with a co-supplier test

cart designed for research purposes. The process was

divided into two parts: inspection planning and exe-

cution. The units are controlled by a module based

on an STM32 microcontroller with an ARM Cortex

M3 core. They employ a SLAM algorithm for lo-

calization and mapping while using MQTT to com-

municate within the modules. Once again, this robot

relies on heavy modules, sensor fusion, and predeter-

mined algorithms to perform its task. It also differs

from our approach as it does not use AI and relies on

more algorithms, more sensors, and heavier modules

to perform the task

Dandurand et al. (Dandurand et al., 2022) present

a system used in Hydro-Quebec of an all-weather au-

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tonomous inspection robot for electrical substations

capable of acting in all climatic situations. In particu-

lar, it aims at substations subject to severe winter con-

ditions in northern countries. The solution uses real-

time kinematic positioning (RTK) for location and an

IMU for attitude awareness. This system also em-

ploys a set of ﬁxed algorithms and does not rely on AI

to perform any task. Although it is also meant for in-

spection, the applications differ from those proposed

in this work in a similar way as the previous ones.

Salimpour (Salimpour et al., 2022) et al. propose

a comprehensive deep-learning framework capable of

monitoring anomalies and changes in environments at

the pixel level in image pairs. They used neural net-

work architectures such as SuperPoint and Superglue

to detect changes and foreign objects in an environ-

ment. A terrestrial robot was used for reference and

query data collection purposes. They employ a com-

plex set of sensors, including two RGB cameras, a LI-

DAR camera, and a rotative LIDAR, to perform their

tasks. Despite this robot employing AI, the study is

not centered on understanding some aspects displayed

in this work, such as real-time constraints and hard-

ware proﬁling for performing these critical tasks.

Cheng et al. (Cheng and Xiang, 2020) designed

a completely autonomous trail-type inspection robot

for monitoring electrical distribution rooms. It is

equipped with multiple sensors, including an optical

zoom camera, a thermal imaging camera, and a par-

tial discharge detector. It has a software layer that in-

terfaces with the hardware to perform inspection ac-

tions, task management, and data visualization. They

adopt computer-vision tools to perform the recogni-

tion tools. It also differs from our work, as its naviga-

tion is attached to ﬁxed trails, and its image process-

ing does not rely on AI.

Finally, Hercitk et al.(Hercik et al., 2022) present

an autonomous industrial mobile robot with the aim

of cooperation with the production line, working on

logistical tasks. For this matter, they used a commer-

cial robot from Mobile Industrial Robots (Mir), in ad-

dition to a centralized control station for MiR robots

(MiR FLEET) and an I/O module for MiR robots

(MiR WISE) to communicate. This application has

a different purpose and functioning when compared

to the work presented in this task.

Several applications relate to our work. Some

works tackle autonomous navigation tasks and even

autonomous inspection tasks. Nonetheless, the re-

viewed appliances differ from the proposal in this

work for several reasons. Our proposal is from a lean

system, with a minimum sensor set, but relying on AI

to perform inspection tasks in an open space. We also

study requirements such as real-time constraints and

hardware proﬁling to understand how the Edge plat-

form supports the proposed tasks.

4 METHODOLOGY

In this section, we will explain how our AMR works

in general. Here we present all its physical attributes,

such as its mechanics, the method of autonomous lo-

comotion used, and the hardware, including actuators,

controllers, sensors, and the Edge AI device.

4.1 Physical Attributes

4.1.1 Mechanics

The robot uses a parallelepiped-shaped metal housing

with the possibility of opening an upper part function-

ing as a trunk. The robot has the following dimen-

sions: 22cm width, 34cm length, and 20cm height.

The robot uses four omnidirectional wheels arranged

in the four corners of the housing. In Figure 1, we

show the robot showing its dimensions. The camera

is positioned in the upper front, and the LIDAR is in

the upper part. The robot weight 3.2kg.

Figure 1: Dimensions of the robot.

The use of omnidirectional wheels provides ex-

cellent handling capacity. In contrast, the mechani-

cal construction of these wheels is more complex, and

they require independent steering and control systems

for each wheel. The omnidirectional wheels type used

on the robot is the mecanum wheels.

4.1.2 Hardwares

As actuators, the robot has 4 DC motors of 12 volts

and 200 rpm, each connected directly and individually

to each wheel. As a motor controller, we have a set

of two pieces of hardware: a Raspberry pi 4 model B

(Raspberrypi, 2023) working together with the Step-

Towards Autonomous Mobile Inspection Robots Using Edge AI

557

per Motor HAT V0.1 (Waveshare, 2023), which per-

forms the driver role.

The Raspberry Pi 4 Model B features a 64-Bit

quad-core processor that runs at frequencies up to

1.5GHz with 4GB RAM. It has a dual-band 2.4/5.0

GHz wireless network, Bluetooth 5.0/BLE, True Gi-

gabit Ethernet, USB 3.0, and USB-C power capabil-

ity. Our controller is a composite hardware that we

can divide into two parts, the Raspberry being the

high-level controller and the Stepper Motor HAT be-

ing the low-level controller.

The Edge AI device is an NVIDIA Jetson Xavier

NX development kit (NVIDIA, 2023). Its CPU is

based on a 6-core NVIDIA Carmel ARM v8.2 64-

bit CPU. It has a GPU with 384 NVIDIA CUDA

Cores and 48 Tensor Cores, and 2 NVDLA (NVIDIA

Deep Learning Accelerator) combined with 8 GB of

LPDDR4x RAM. Its connectivity is Gigabit Ethernet

and WiFi/Bluetooth interface (M.2 Key E), 4 USB 3.1

ports, HDMI and DP video output. As sensing units,

we have a YDLIDAR X2 (YDLIDAR, 2023) with a

range frequency of 3000Hz, scanning frequency of 5-

8Hz, range of 0.12-8m and a 30FPS Logitech C920

camera with full HD resolution. A power bank pow-

ers the Raspberry with a capacity of 10000mAh with

DC5V 2.1A output. The robot also has two 11.1V

Lipo batteries.

The sensors are positioned on the upper outer part

of the robot and are connected to Jetson. Jetson works

as an Edge AI platform processing sensor data and ex-

ecuting the navigation methodology. A Jetson is con-

nected to the controllers (Raspberry + Stepper Mo-

tor HAT) via an Ethernet cable that goes directly to

the Raspberry and sends the individual powers to be

transmitted by each motor at each moment. In the ta-

ble 1, we show the function performed by each hard-

ware. We observed that, in this table, each level is

related to the levels positioned directly above and be-

low.

Table 1: Hardware roles table.

ROLE DEVICES

Actuators 4 DC Motors

Low Level Controller Stepper Motor HAT

High Level Controller Raspberry Pi 4

Edge AI Device NVIDIA Jetson Xavier NX

Sensing C

amera + LIDAR

4.1.3 Locomotion Method

The robot locomotion method is based on image ob-

ject detection using a CNN followed by displacement

towards this object until the robot is close enough to

perform an inspection or other task. The objects de-

tected in the image represent the objective locations of

robot positioning. We use YOLOv7 CNN to perform

object detection(Wang et al., 2022). A signiﬁcant ad-

vantage of YOLOv7 is that it can be easily trained us-

ing images of the target locations of the robot’s move-

ment, for example, machines and speciﬁc objects pho-

tos in an industrial plant. This work uses trafﬁc cone

images to simulate the target locations. The locomo-

tion method follows the logic of a state machine. This

state machine is presented in Figure 2. We explain

each state in detail and the change of states below.

Figure 2: Locomotion method state machine.

Pre Conﬁgured Movement: Our locomotion

method has as input a set N where each element n

a list of previously conﬁgured sequential movements.

This step is the current way the robot handles ob-

stacle avoidance. As the operating plan is known in

advance, a sequence of previously conﬁgured move-

ments can be used if it is necessary to deviate from

an object until the camera can visualize the follow-

ing objective location. This sequence lists movements

in the four directions and for previously determined

times. At each entry in this state, the robot executes

the list of movements n

. The set of previously con-

ﬁgured movements is empty if there is no obstacle at

the operation site. After this state, the robot enters the

search state.

Search: This state is where the robot searches for

the following target location. For this, in this state,

the robot rotates around its axis until the next ob-

ject representing the following objective location is

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detected. This rotation around its axis is performed

non-continuously, with short pauses so that the cam-

era image is not blurred, which makes object detec-

tion difﬁcult. After the robot detects the object, it will

move in its direction. This task is performed in the

tracking control stage.

Tracking Control: This state represents the

movement of the robot toward the target location. The

control method used is feedback control. The variable

to be controlled here is the horizontal center of the de-

tected object. We compare the horizontal center value

of the object with the desired value, which is the cen-

ter of the image. If the object is positioned more to

the right of the image, more power must be applied to

the wheels on the left side of the robot and vice versa.

Equation 1 deﬁnes the percentage of total power

RP that is applied to the right side motors and Formula

2 deﬁnes the percentage of total power applied to the

left side motors. Where ld is the horizontal distance

from the center of the detected object to the left edge

of the image and rd is the distance from the center

of the object to the right edge of the image. Figure 3

shows the cone detection and the measurements used

to deﬁne the power applied to each motor.

Figure 3: Cone detection and the measures used to deﬁne

the motors power at tracking control stage.

RP = min(200 ∗ (rd/iw),100) (1)

LP = min(200 ∗ (ld/iw), 100) (2)

The tracking control state control model follows

the traditional feedback control model shown in Fig-

ure 4. Where r is the reference value, in this case, the

horizontal center of the image, e the error calculated

between the horizontal center of the image and the

horizontal center of the detected object, u is the ac-

tion of control calculates in Equations 1 and 2, y the

update of the variable and Y m the new measurement

of the variable.

The robot remains in the tracking control state un-

til one of two conditions is met. The ﬁrst condition

is until it is in the target location. The deﬁnition of

arrival at the target location is given by the LIDAR

Figure 4: Traditional feedback control model applied to our

AMR.

reading, indicating that the robot is at a close distance

from the detected object. If this condition is met, it

passes to the task execution state. The second condi-

tion is when the robot loses visibility of the detected

object. If this happens, it returns to the search state.

These conditions are shown in Figure 2.

Task Execution: The execution of the task in this

work is abstracted and can be any task to be per-

formed by the robot in an industrial plant, for ex-

ample, performing a visual or physical inspection of

machines, reading data, delivering materials or tools,

among other possible tasks. After ﬁnishing the execu-

tion of the task, the robot will ﬁnish its work if all the

tasks have already been executed. Otherwise, it will

go to the pre-conﬁgured movement state, as shown in

Figure 2.

5 EXPERIMENTAL RESULTS

To run our tests, we trained YOLOv7 with trafﬁc cone

images, using 584 images collected from the internet,

each containing one cone or more, 537 for training

and 47 for validation. The trained model was the

YOLOv7-Tiny model. The YOLOv7 uses the Py-

Torch framework with GPU support. The applica-

tion was developed using Python3 and the OpenCV

library.

To validate the methodology, we used four trafﬁc

cones representing the target locations for robot lo-

comotion. Functioning and ability to travel to target

locations were performed in a laboratory. The loco-

motion method proved to be fully capable of moving

to all objective locations in sequence. The use of pre-

conﬁgured movements proved to be capable of avoid-

ing obstacles but requires prior knowledge of the op-

erating site. Occlusion of the detection object can also

be circumvented with the list of pre-conﬁgured mo-

tions. To avoid using predetermined movements, it

will be necessary to implement a navigation method

that uses LIDAR data with a SLAM (Aulinas et al.,

2008) algorithm.

The operating system used was Ubuntu 18.04

LTS. This operating system has been installed with

default conﬁguration and by default it comes with

Towards Autonomous Mobile Inspection Robots Using Edge AI

559

graphical user interface. his operating system in idle

state has an average RAM consumption of 19%, CPU

consumption is 1% and GPU consumption is 0%.

In order to evaluate the performance of our Edge

AI device, we performed proﬁling and FPS tests of the

application. Both are extremely important in robotics

because proﬁling shows how much hardware the ap-

plication uses, displaying whether there is the ability

to study the addition of more sensors and more pro-

cessing methods. On the other hand, FPS shows us

how fast the system is processing information from

the environment. In proﬁling, the measurements used

were the average usage of the 6 CPU cores, average

RAM, and average GPU usage.

In the tests, we used two types of implementa-

tions. The ﬁrst is a sequential implementation where

object detection in the image always occurs after im-

age capture. The second is a parallel implementa-

tion where capture and detection occur in concurrent

and synchronized threads so that each captured frame

is processed only once. These two implementation

modes were executed in 3 different image capture res-

olutions, 720x480, 1280x720, and 1920x1080. The

Jetson Xavier NX power mode used for the tests was

the 20 watts mode using six cores.

In Table 2, we can see the average FPS rate for

each tested implementation and resolution. Notably,

the best FPS rate is the parallel implementation with

720x480 resolution. The 720x480 resolution was the

only one where the average detection time was lower

than the average capture time. In this way, the low-

est resolution with parallel implementation was the

only conﬁguration where the bottleneck was the de-

tection of the object itself. The performance was bet-

ter because the capture and detection happen simulta-

neously in the parallel implementation.

On the other hand, the sequential implementation

could not reach the same speed because the capture

time added to the detection time directly inﬂuences

the FPS rate. The average time spent in detection

does not vary as much as the capture time as we in-

crease the resolution. This result happens because

the YOLOv7 detection works on a default image size

when performing the inference of 640x640. Every

image submitted to detection is resized to these di-

mensions. For the 1280x720 and 1920x1080 resolu-

tions, we noticed that the bottleneck of the parallel

method is the image capture time, which is similar to

the sums of the capture and detection times of the se-

quential method. This behavior proves that YOLOv7

achieves real-time requirements on Jetson Xavier NX

for object detection.

In Figures 5, 6, 7, 8, 9, and 10, we can observe the

hardware consumption within a window of 1 second

Table 2: FPS, capture and process time table. In the ﬁrst row

we have the different resolutions, ’SEQ.’ refers to sequen-

tial, ’PARL.’ refers to parallel. Process and capture time are

in milliseconds.

Res 720x480 Res 1280x720 Res 1920x1080

SEQ. PARL SEQ. PARL SEQ. PARL

FPS 19.99 25.47 9.99 9.99 4.99 4.99

Process t. (ms) 39.01 38.79 40.69 41.19 42.59 42.41

Capture t. (ms) 10.89 12.07 59.22 99.97 157.30 200.09

with 50 samples and 20 milliseconds between them.

Figure 5: Proﬁling for 720x480 resolution with sequential

implementation.

Figure 6: Proﬁling for 720x480 resolution with parallel im-

plementation.

In Figures 5 and 6, we have proﬁling results for

the 720x480 resolution. Note that there is no GPU

idle time in the sequential implementation, which ob-

tained the highest FPS among all tests. In the sequen-

tial implementation, there are moments of idleness

where the GPU reaches 0% consumption. The aver-

age CPU consumption was also higher in the parallel

implementation.

Figures 7 and 8 show the results of proﬁling the

1280x720 resolution. Both implementations show

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Figure 7: Proﬁling for 1280x720 resolution with sequential

implementation.

Figure 8: Proﬁling for 1280x720 resolution with parallel

implementation.

Figure 9: Proﬁling for 1920x1080 resolution with sequen-

tial implementation.

moments of GPU idleness and similar average CPU

consumption.

Figures 9 and 10 show the results of proﬁling the

Figure 10: Proﬁling for 1920x1080 resolution with parallel

implementation.

1980x1080 resolution. Even in the 1280x720 resolu-

tion, there are moments of GPU idleness. The par-

allel implementation was the one with the lowest av-

erage CPU consumption, which indicates the reason

for having the longest capture time among all exper-

iments. The sequential implementation had the most

signiﬁcant variation in average CPU usage. The aver-

age use of RAM remains the same in all tests. This

use is 61%.

6 CONCLUSIONS

In this work, we present a robot capable of moving

to speciﬁc locations in an industrial plant. This robot

applies the Edge AI concept in its architecture. Its

handling is based on a state machine and uses com-

puter vision and feedback control.

The solution employs computer vision to detect

objects representing speciﬁc locations in an industrial

plant. This detection happens through the use of a

CNN, more speciﬁcally, YOLOv7. After identifying

the local objective, the feedback control works so that

the robot moves to the local objective. The robot was

tested in the laboratory and could perform a move-

ment task in a controlled environment.

The system’s performance test presented here

evaluated image capture and object detection within

three different camera resolutions and with two dif-

ferent implementations, in a sequential and parallel

way. The effects of using different implementations

on object detection performance were presented.

The best result was 25 FPS, with parallel imple-

mentation and 720x480 resolution. This information

shows that YOLOv7 achieves real-time requirements

on Jetson Xavier NX for object detection. Addition-

ally, a proﬁle of the tests was also performed, compar-

Towards Autonomous Mobile Inspection Robots Using Edge AI

561

ing the CPU, RAM, and GPU consumption for each

resolution and type of implementation.

There is a wide range of future work. First, valida-

tion in a real environment can improve the evaluation

of its operation in a more challenging scenario. The

application in mine scenarios, workshops, and ware-

houses is of great value and can bring insights. Based

on sensor fusion, better use of LIDAR can be inte-

grated to achieve fully autonomous navigation where

no movement-related data input will be required. It is

also viable to study the use of other sensors. Finally,

we will apply and validate Reinforcement Learning

and other AI techniques and add probabilities to the

state machine transforming it into Markov Chains to

improve the robot’s functioning.

ACKNOWLEDGEMENTS

The authors would like to thank SASCAR, NVIDIA,

UFOP, CAPES and CNPq for supporting this work.

This work was partially ﬁnanced by Coordenac¸

de Aperfeic¸oamento de Pessoal de N

ıvel Superior

(CAPES) - Finance Code 001, and by Conselho Na-

cional de Desenvolvimento Cient

ıﬁco e Tecnol

ogico

(CNPq) - Finance code 308219/2020-1.

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