Robotic navigation algorithm with machine vision

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 2, April 2020, pp. 1308~1316
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i2.pp1308-1316  1308
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Robotic navigation algorithm with machine vision
César G. Pachón-Suescún, Carlos J. Enciso-Aragón, Robinson Jiménez-Moreno
Faculty of Engineering, Nueva Granada Military University, Bogotá D.C., Colombia
Article Info ABSTRACT
Article history:
Received Jun 7, 2019
Revised Oct 11, 2019
Accepted Oct 20, 2019
In the field of robotics, it is essential to know the work area in which
the agent is going to develop, for that reason, different methods of mapping
and spatial location have been developed for different applications. In this
article, a machine vision algorithm is proposed, which is responsible for
identifying objects of interest within a work area and determining the polar
coordinates to which they are related to the observer, applicable either with
a fixed camera or in a mobile agent such as the one presented in this
document. The developed algorithm was evaluated in two situations,
determining the position of six objects in total around the mobile agent.
These results were compared with the real position of each of the objects,
reaching a high level of accuracy with an average error of 1.3271% in
the distance and 2.8998% in the angle.
Keywords:
Embedded System
Morphological filters
Robotic navigation
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Robinson Jiménez Moreno,
Mechatronics Engineering Program, Faculty of Engineering,
Nueva Granada Military University,
Carrera 11 #101-80, Bogotá D.C., Colombia.
Email: robinson.jimenez@unimilitar.edu.co
1. INTRODUCTION
Currently, in the field of robotics, it can be found a series of methods to map a specific work area
and, from this data, perform a processing according to the application in which the robotic agent will be
developed. It is essential to extract this data accurately because the movement of the robotic agent depends
on this data as seen in [1], where three robots map a labyrinth together in order to solve it. Most of
the algorithms developed today are based on a system of integrated sensors, such as for example
ultrasonic [2] or laser as is the case of [3]. At the national level, some examples of these situations can be
seen [4-5], which generates a series of limitations when dealing with certain situations, such as distinguishing
between two different types of objects. Since they are low-cost systems and that have extensive
documentation in terms of instrumentation and mathematical modeling as mentioned in [6], they become
the primary choice. On the other hand, there are algorithms based on a global camera as implemented in [7],
which can cause that in environments where it is not possible to use a camera in that position, strategies that
may increase the complexity of the system or restrict its functionality should be sought. Focusing studies on
the design of environmental mapping algorithms and identification of this allows that algorithms such as
the one presented in [8], where a trajectory planning algorithm is designed in a virtual environment, can be
implemented in real environments.
This article proposes an alternative method to solve this problem that will be focused on
the implementation of individual mobile agents whose task will be to identify specific objects within an
established work area. To do this, an algorithm based on machine vision techniques is designed and through
experimental tests, the necessary relationships are determined to make possible the equivalence between
the real locations of each object versus that calculated by the algorithm.
In the state of art, many works about mobile robots are done. The main idea is making its auto self-
driving [9, 10] using planning trajectories for this task, in 2D [11] and 3D [12] environments, considering
energy-aware [13], terrains characteristics [14] and implementing optimization methods [15]. However,

Int J Elec & Comp Eng ISSN: 2088-8708 
Robotic navigation algorithm with machine vision (César G. Pachón-Suescún)
1309
machine vision systems are very useful for auto self-driving, to control the mobile robot [16] and avoid
obstacles [17], such as presented in the present work. The article is divided into four main parts, the first part
presents some theoretical foundations necessary for the understanding of the other stages. The second one
focuses on the materials and methods, where the elements used for the tests and the calculations made for
the detection of the objects of interest are shown. The third part shows the results obtained and two examples
of cases in which the algorithm was tested. Finally, the conclusions regarding the designed algorithm are
presented.
2. THEORETICAL FRAMEWORK
The algorithm is mostly developed on the OpenCV libraries for Python, since being a mobile agent
independent of an external console, these software tools are a main alternative to be applicable in an
embedded system. For the development of the algorithm, the fundamental bases of image processing were
taken into account.
2.1. Color filters
Color filters are those that allow a specific color to be identified within a digital image, generally,
these filters have a lower and upper range by which they limit which color or colors are those that are to be
determined within the image. These ranges are defined according to a particular color scale, there are a wide
variety of color scales, among which the most common are RGB and HSV, but each scale has its own
characteristics that make each one have different applicability [18]. Table 1 shows the main advantages and
disadvantages of the three color models that were considered to develop the algorithm.
Table 1. Advantages and disadvantages of three color models, based on [18]
Model Advantages Disadvantages
RGB -It is used in the video screen due to its additive properties.
-It is considered as a computationally practical model.
-It is not useful for specifying objects and recognizing
colors.
-It is difficult to determine a specific color.
HSV Colors easily defined by human perception, unlike RGB. Indefinite achromatic tone points are sensitive to
deviations of RGB values and hue instability, due to
the angular nature of the characteristic.
HSL -The chrominance components (H and S) are associated
with the way humans perceive, it is perfect for image
processing applications.
-The component (Hue) can be used to perform the
segmentation process instead of the three components that
make up the model.
-The indefinite achromatic tone points are sensitive to
deviations of RGB values and tonality instability, due
to the angular nature of the characteristic.
-It is not uniform.
2.2. Morphological filters
These kinds of filters are commonly used in machine vision algorithms, they can perform different
tasks depending on the filter applied, either eliminate the noise in an image [19] or identify the geometric
structure of a given object [20]. It should be noted that this kind of filters is applied only on binarized images,
i.e. images in which only the absolute white or black color is given, that is equivalent to 1 and 0, respectively.
The morphological filters are theoretically an n-dimensional matrix whose structuring element can be circular
or square, or even irregular, which can vary depending on the treatment that it is wanted to perform or
the characteristics wanted to extract from the image, such as those observed in [21, 22].
In Figure 1, an example of how the most common morphological filters that are frequently used in
applications related to object recognition work can be seen. On one hand, erosion is a matrix operation
between pixels whose function is to reduce the number of white pixels by evaluating the proximity of each of
them to the black pixels, depending on the structuring element see Figure 1b. On the other hand, the dilation
operation works in a completely opposite way to the erosion, reason why the quantity of white pixels
increases see Figure 1c.
Figure 1. Morphological filters. (a) Original image, (b) erosion filter applied to the original image
and (c) dilation filter applied to the original image [23]

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 2, April 2020 : 1308 - 1316
1310
3. MATERIALS AND METHODS
The algorithm developed is framed in a mobile robotics project, for this reason, there are a series of
conditions for the development of the same, including the camera is in the structure of the agent in the lower
frontal part, the agent has an embedded Raspberry Pi 3 system [24], which is responsible for performing all
the necessary calculations for the different algorithms. The work area is 2 m² on flat terrain, but may have
slight changes in lighting and the objects to be identified will be uniform cubic structures of magenta color.
It should be noted that the algorithm was developed taking into account these guidelines, but this can be
implemented in other types of robotic agents and work areas of different dimensions.
The agent must identify all the objects of interest within the work area, the identification algorithm
starts taking 6 captures at 60 degree intervals, since the focus of the camera (Raspberry Pi Camera V2) has an
approximate focus of 66 degrees, thus covering the entire perimeter around the mobile agent. As a first step,
it is necessary for the agent to start taking captures of their environment, in this case, they were made with
a resolution of 640x480 pixels. In Figure 2, one of these images can be observed, with which the procedure to
identify the object will be explained later.
Figure 2. Original capture of the work area
For the identification of the objects of interest present in the image, it is necessary to propose
a series of filters that allow determining with precision and accuracy in which position they are with respect
to the mobile agent. In the majority of the applications related to machine vision and robotics, it is pertinent
to comply with these two parameters to a lesser or greater extent, depending on the application to which it is
focused, for this particular case, the values obtained must have a minimum error, given that these values are
what will determine how the mobile agent should move within the work area without crashing and reaching
a certain point. Based on this, a color filter is implemented that allows the use of the algorithm even when
there are slight changes in ambient lighting. The color scale that best suited the needs was the HSL see
Figure 3.
Figure 3. HSL color scale [25]

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From this chart, the following ranges were defined for each color parameter:
𝐻1= 260 to 𝐻2= 310
𝑆1= 0 to 𝑆2= 0.75
𝐿1 = 0.47 to 𝐿2= 1
These parameters in OpenCV use other ranges that are from 0 to 180 for H and from 0 to 255 for both S and
L, so the ranges that are finally applied in the programming are the following:
𝐻1= 130 to 𝐻2= 155
𝑆1= 0 to 𝑆2= 190
𝐿1= 120 to 𝐿2= 255
Applying this color filter, it is obtained the result observed in Figure 4a. In the image obtained,
small groups of pixels can be observed that are not part of the object, for that reason, they are considered as
noise, being eliminated by morphological filters. First, an erosion operation is implemented with a 4x4 square
structuring element see Figure 4b and finally, a dilatation operation with a 4x4 square structuring element to
try to recover the original dimensions of the object. Once these morphological filters are applied,
a remarkable change can be observed in Figure 4c.
Figure 4. Application of morphological filters. a) Binarized original image.
b) Application of the erosion filter. c) Application of the dilation filter.
When all the identified segmented color figures are found, another problem must be faced: it is
possible that there are objects of the same color near the work area, but with different shapes that would not
correspond to the defined objects of interest, therefore, it is necessary to implement an additional filter that
allows discriminating other objects that may have the same color. The filter that is decided to be implemented
is based on the forms, which works in the following way: from Figure 4, the contours of the different
elements present in the image are extracted. With the points that make up the contours, each of them is
replaced forming line segments, finally, a number of lines and certain intersections are obtained. Therefore,
it is useful to identify when it is an object of interest given that for this case they are cubes and in the image
that is obtained through the treatment of images, an element with four edges is observed see Figure 5.
Figure 5. In the initial picture as a visual test, the contours of
the objects identified as objects of interest are indicated.

 ISSN: 2088-8708
1312
When it is possible to determine that the objects present in the image are actually cubes, it is sought
to apply the ratio of pixels to metric units to determine the distance to which the different objects present in
the image are located. This relationship was established experimentally by taking capture of a cube aligned
on the X-axis with the camera at various known distances. Additionally, the lower and upper-end points on
the Y-axis were found for the object in the image, this in order to determine how many pixels represented
the height of the cube, since this dimension remains invariant for the camera independently from what angle
the image was taken. From these experimental data, a graph with its respective trend line and the equation
that describes it were obtained see Figure 6.
From this data, a mathematical equation that relates the pixels with the distance from the camera to
the object in a precise and exact way was obtained. Based on the image processing and taking into account
the calculated ratio between metric units and pixels captured by the camera, the position of each object of
interest is calculated using the mobile agent as a reference point see Figure 7. Based on Figure 7, a series of
equations are proposed in order to calculate the polar coordinates of the object with respect to the agent in
metric units, where pix=height in pixels of the figure. Ca refers to the adjacent leg generated from the focus
of the camera to the visible face of the object of interest. Therefore, equation 1 describes the value of
the variable Ca.
Figure 6. Graph of relationship between number of
pixels and real distance to the cube in meters with
exponential trend line
Figure 7. Illustration of the notation used for
the calculations made
𝐶𝑎 = 35.808 ∗ 𝑝𝑖𝑥−0.997 (1)
C is the opposite leg of the center of the camera see (2).
𝐶𝑎 = tan(33) 𝐶𝑎 (2)
𝑋𝑐𝑜𝑜𝑟 is the X coordinate in pixels to the center of the visible face. Taking into account that in the X-axis
the image has 640 pixels, a conversion of pixels to metric units is made see (3)
𝑋 =
(𝑋𝑐𝑜𝑜𝑟)(2𝐶)
640
(3)
Co is the distance from the center of the camera's focus to the center of the object see (4)
𝐶𝑜 = 𝑋 − 𝐶 (4)
Once the values of the adjacent leg and the opposite leg have been calculated, the polar coordinates
are calculated with equations 5 and 6, taking into account that A refers to the angle that the agent has rotated
between each capture.
𝛽 = tan−1 (
𝐶𝑜
𝐶𝑎
) + 𝐴 (5)

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ℎ = √ 𝐶𝑜2 + 𝐶𝑎2 (6)
Finally, the possible case is raised where one of the objects of interest can be captured in more than
one image, so the mobile agent would interpret that there are more cubes than the real amount that is in
the work area. To avoid this drawback, a filter is defined consisting of the condition that if two consecutive
cubes with an angle difference of less than 10 degrees and a distance of ± 5 cm are detected in two
consecutive captures, they are treated as the same cube, thus removing both from the list and determining an
average between the two calculated positions. If, on the contrary, one of these two conditions is not met,
they will be different cubes and both positions will be maintained in the list of objects. In the list of objects,
it can be observed the polar coordinates of each one with respect to the mobile agent, taking into account that
zero degrees starts aligned with the camera in the first capture and will increase in counterclockwise direction
until completing the 360 degrees.
4. RESULTS AND DISCUSSION
What is sought is that the algorithm is generic, due to this, the number of objects in the work area
can become n, where n→∞, for this reason, for the tests, a specific sample size is not established.
The algorithm was put to tests in a real and controlled environment with an area of 2m², where 3 cubes were
randomly distributed within the work area, in the center the mobile agent was located and it was looked to
check the measurements given by the algorithm to compare them with the real distances in which each cube
was located. In Figure 8, the first case studied is observed.
Figure 8. First case study in a real work area.
Once the objects were located in random places, the algorithm was executed, and the results
obtained were compared with the real measurements that were taken experimentally, calculating
the approximate error as shown in Table 2.
Table 2. Comparison of the measurements taken from
the algorithm with the real measurements for the first case study
Object Angle
Algorithm Real (approx.) % Error
1 29.4435 30 1.8550
2 142.1999 147 3.2653
3 282.4787 281 0.5262
Distance (cm)
1 33.0166 31.5 4.8146
2 42.6121 40.8 4.4414
3 31.7967 32.4 1.8620

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As shown in the previous table, the error is less than 5%. This represents that the level of precision
and accuracy with which the algorithm detects the distance of objects from the agent is feasible for its
implementation in the established work environment. Additionally, a second case was posed where
the objects are positioned in such a way that the same cube is observed in two different frames, this in order
to verify that the data taken by the algorithm contains the correct amount of information and that they
effectively approximate the real values. In Figure 9, the new distribution of the objects within the work area
can be observed. Again, the results were tabulated in order to obtain the percentage of error between the real
measurement and that calculated by the machine vision algorithm, as can be seen in Table 3. Once the data of
the two tests have been recorded, a maximum error in distance of 4.8146% can be evidenced, which is
equivalent to 1.5166 cm, and 3.2653% error in angle, equivalent to 4.8 degrees. On the other hand,
the average error in distance is 1.3271% and in angle, it is 2.8998%, which would allow implementing this
algorithm without compromising the correct operation and mobility of the robot within the work area.
Figure 9. Second case study in a real work area
Table. 3. Comparison of the measurements taken from
the algorithm with the real measurements for the second case study
Angle
1 96.2896 96 0.3017
2 233.3020 230 1.4357
3 267.5392 266 0.5786
Distance (cm)
1 28.1190 27.5 2.2509
2 18.8660 19.2 1.7395
3 37.2338 36.4 2.2907
5. CONCLUSION
The developed algorithm is a valid starting point for tracking applications in the field of robotics
that can be focused on tasks of grouping and evasion, since it allows identifying specific objects and, from
these data, can determine how to maneuver or interact with them. It should be noted that the implementation
of the algorithm has a relatively high cost compared to algorithms based on ultrasonic sensors, mainly due to
the implementation of a camera and, in this case, an embedded system for its management. On the other
hand, by implementing these two tools in a mobile agent, an average error of 1.3271% for the distance
measurement and 2.8998% for the measurement of angles was obtained.
In comparison with algorithms developed with the help of a global camera, this has the advantage
that it avoids the implementation of a communication system between the mobile agent and an external
terminal that performs image processing. In addition, this type of architecture allows the applicability of this
algorithm in tasks whose environments do not allow the use of a globalized camera. Although the algorithm
allows to identify the polar coordinates of the objects of interest around it, it is necessary to design additional
strategies to identify the elements when it is impossible to detect them in the first capture sampling of
the work area, either by an obstacle, irregularities in the field or an external agent

1315
ACKNOWLEDGEMENTS
The authors are grateful to the Nueva Granada Military University for the support given in
the development of this work.
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BIOGRAPHIES OF AUTHORS
César Giovany Pachón Suescún was born in Bogotá, Colombia, in 1996. He received his
degree in Mechatronics Engineering from the Pilot University of Colombia in 2018.
Currently, he is studying his Master’s degree in Mechatronics Engineering and working as
Research Assistant at the Nueva Granada Military University with an emphasis on Robotics
and Machine Learning.E-mail: u3900259@unimilitar.edu.co
Carlos Javier Enciso Aragón was born in Bogotá, Colombia, in 1996. He received his
degree in Mechatronics Engineering from the Pilot University of Colombia in 2018.
Currently, he is studying his Master’s degree in Mechatronics Engineering and working as
Research Assistant at the Nueva Granada Military University with an emphasis on Robotics
and Machine Learning. E-mail: u3900256@unimilitar.edu.co
Robinson Jiménez Moreno was born in Bogotá, Colombia, in 1978. He received
the Engineer degree in Electronics at the Francisco José de Caldas District University - UD -
in 2002. M.Sc. in Industrial Automation from the Universidad Nacional de Colombia - 2012
and Ph.D. in Engineering at the Francisco José de Caldas District University - 2018. He is
currently working as a Professor in the Mechatronics Engineering Program at the Nueva
Granada Military University - UMNG. He has experience in the areas of Instrumentation and
Electronic Control, acting mainly in Robotics, control, pattern recognition, and image
processing. E-mail: robinson.jimenez@unimilitar.edu.co

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