
This paper presents an autonomous navigation method for an agricultural mobile robot "AgriEco Robot", with four-wheel-drive and embedded perception sensors. The proposed method allows an accurate guidance between strawberry crop rows while automatically spraying pesticides, as well as detecting the end and switching to the next rows. The main control system was developed using Robot Operating System (ROS) based on a 2D LIDAR sensor. The acquired 2D point clouds data is processed to estimate the robot's heading and lateral offset relative to crop rows. A motion controller is incorporated to ensure the developed autonomous navigation method. Performance in terms of accuracy of the autonomous navigation has been evaluated in real-world conditions within strawberry greenhouses, proving its usefulness for automatic pesticide spraying.
Citation: Abdelkrim Abanay, Lhoussaine Masmoudi, Mohamed El Ansari, Javier Gonzalez-Jimenez, Francisco-Angel Moreno. LIDAR-based autonomous navigation method for an agricultural mobile robot in strawberry greenhouse: AgriEco Robot[J]. AIMS Electronics and Electrical Engineering, 2022, 6(3): 317-328. doi: 10.3934/electreng.2022019
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This paper presents an autonomous navigation method for an agricultural mobile robot "AgriEco Robot", with four-wheel-drive and embedded perception sensors. The proposed method allows an accurate guidance between strawberry crop rows while automatically spraying pesticides, as well as detecting the end and switching to the next rows. The main control system was developed using Robot Operating System (ROS) based on a 2D LIDAR sensor. The acquired 2D point clouds data is processed to estimate the robot's heading and lateral offset relative to crop rows. A motion controller is incorporated to ensure the developed autonomous navigation method. Performance in terms of accuracy of the autonomous navigation has been evaluated in real-world conditions within strawberry greenhouses, proving its usefulness for automatic pesticide spraying.
To exemplify the phenomena of compounds scientifically, researchers utilize the contraption of the diagrammatic hypothesis, it is a well-known branch of geometrical science named graph theory. This division of numerical science provides its services in different fields of sciences. The particular example in networking [1], from electronics [2], and for the polymer industry, we refer to see [3]. Particularly in chemical graph theory, this division has extra ordinary assistance to study giant and microscope-able chemical compounds. For such a study, researchers made some transformation rules to transfer a chemical compound to a discrete pattern of shapes (graph). Like, an atom represents as a vertex and the covalent bonding between atoms symbolized as edges. Such transformation is known as molecular graph theory. A major importance of this alteration is that the hydrogen atoms are omitted. Some chemical structures and compounds conversion are presented in [4,5,6].
In cheminformatics, the topological index gains attraction due to its implementations. Various topological indices help to estimate a bio-activity and physicochemical characteristics of a chemical compound. Some interesting and useful topological indices for various chemical compounds are studied in [3,7,8]. A topological index modeled a molecular graph or a chemical compound into a numerical value. Since 1947, topological index implemented in chemistry [9], biology [10], and information science [11,12]. Sombor index and degree-related properties of simplicial networks [13], Nordhaus–Gaddum-type results for the Steiner Gutman index of graphs [14], Lower bounds for Gaussian Estrada index of graphs [15], On the sum and spread of reciprocal distance Laplacian eigenvalues of graphs in terms of Harary index [16], the expected values for the Gutman index, Schultz index, and some Sombor indices of a random cyclooctane chain [17,18,19], bounds on the partition dimension of convex polytopes [20,21], computing and analyzing the normalized Laplacian spectrum and spanning tree of the strong prism of the dicyclobutadieno derivative of linear phenylenes [22], on the generalized adjacency, Laplacian and signless Laplacian spectra of the weighted edge corona networks [23,24], Zagreb indices and multiplicative Zagreb indices of Eulerian graphs [25], Minimizing Kirchhoff index among graphs with a given vertex bipartiteness, [26], asymptotic Laplacian energy like invariant of lattices [27]. Few interesting studies regarding the chemical graph theory can be found in [28,29,30,31,32].
Recently, the researchers of [33] introduced a topological descriptor and called the face index. Moreover, the idea of computing structure-boiling point and energy of a structure, motivated them to introduced this parameter without heavy computation. They computed these parameters for different models compare the results with previous literature and found approximate solutions with comparatively less computations. This is all the blessings of face index of a graph. The major concepts of this research work are elaborated in the given below definitions.
Definition 1.1. [33] Let a graph having face, edge and vertex sets notation with respectively. It is mandatory that the graph is connected, simple and planar. If from the edge set is one of those edges which surrounds a face, then the face from the face set is incident to the edge Likewise, if a vertex from the vertex set is at the end of those incident edges, then a face is incident to that vertex. This face-vertex incident relation is symbolized here by the notation The face degree of in is described as which are elaborated in the Figure 1.
Definition 1.2. [33] The face index for a graph is formulated as
In the Figure 1, we can see that there are two faces with degree exactly two with five count and four with count of 6. Moreover, there is an external face with count of face degree which is the count of vertices.
As the information given above that the face index is quite new and introduced in the year 2020, so there is not so much literature is available. A few recent studies on this topic are summarized here. A chemical compound of silicon carbides is elaborated with such novel definition in [34]. Some carbon nanotubes are discussed in [35]. Except for the face index, there are distance and degree-based graphical descriptors available in the literature. For example, distance-based descriptors of phenylene nanotube are studied in [36], and in [37] titania nanotubes are discussed with the same concept. Star networks are studied in [38], with the concept of degree-based descriptors. Bounds on the descriptors of some generalized graphs are discussed in [39]. General Sierpinski graph is discussed in [40], in terms of different topological descriptor aspects. The study of hyaluronic acid-doxorubicin ar found in [41], with the same concept of the index. The curvilinear regression model of the topological index for the COVID-19 treatment is discussed in [42]. For further reading and interesting advancements of topological indices, polynomials of zero-divisor structures are found in [43], zero divisor graph of commutative rings [44], swapped networks modeled by optical transpose interconnection system [45], metal trihalides network [46], some novel drugs used in the cancer treatment [47], para-line graph of Remdesivir used in the prevention of corona virus [48], tightest nonadjacently configured stable pentagonal structure of carbon nanocones [49]. In order to address a novel preventive category (P) in the HIV system known as the HIPV mathematical model, the goal of this study is to offer a design of a Morlet wavelet neural network (MWNN) [50].
In the next section, we discussed the newly developed face index or face-based index for different chemical compounds. Silicate network, triangular honeycomb network, carbon sheet, polyhedron generalized sheet, and generalized chain of silicate network are studied with the concept of the face-based index. Given that the face index is more versatile than vertex degree-based topological descriptors, this study will aid in understanding the structural characteristics of chemical networks. Only the difficulty authors will face to compute the face degree of a generalized network or structure, because it is more generalized version and taking degree based partition of edges into this umbrella of face index.
Silicates are formed when metal carbonates or metal oxides react with sand. The which has a tetrahedron structure, is the fundamental chemical unit of silicates. The central vertex of the tetrahedron is occupied by silicon ions, while the end vertices are occupied by oxygen ions [51,52,53]. A silicate sheet is made up of rings of tetrahedrons that are joined together in a two-dimensional plane by oxygen ions from one ring to the other to form a sheet-like structure. The silicate network symbol, where represents the total number of hexagons occurring between the borderline and center of the silicate network The silicate network of dimension one is depicted in Figure 2. It contain total vertices are edges. Moreover, the result required is detailed are available in Table 1.
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Theorem 2.1. Let be the silicate network of dimension Then the face index of is
Proof. Consider the graph of silicate network with dimension Suppose denotes the faces of graph having degree that is, and denotes the number of faces with degree The graph contains three types of internal faces and single external face which is usually denoted by
If has one dimension then sum of degree of vertices incident to the external face is and when has two dimension then sum of degree of incident vertices to the external face is whenever has three dimension then sum of degree of incident vertices to the external face is Similarly, has dimension then sum of degree of incident vertices to the external face is
The number of internal faces with degree in each dimension is mentioned in Table 1.
By using the definition of face index we have
Hence, this is our required result.
A chain silicate network of dimension is symbolized as which is made by arranging tetrahedron molecules linearly. A chain silicate network of dimension with where denotes the number of rows and each row has number of tetrahedrons. The following theorem formulates the face index for chain silicate network.
Theorem 2.2. Let be the chain of silicate network of dimension Then the face index of the graph is
Proof. Let be the graph of chain silicate network of dimension with where represents the number of rows and is the number of tetrahedrons in each row. A graph for contains three type of internal faces and with one external face While for it has four type of internal faces and with one external face We want to evaluate the algorithm of face index for chain silicate network. We will discuss it in two different cases.
Case 1: When has one row with number of tetrahedrons as shown in the Figure 3.
The graph has three type of internal faces and with one external face The sum of degree of incident vertices to the external face is and number of faces are and Now the face index of the graph is given by
Case 2: When has more than one rows with number of tetrahedrons in each row as shown in the Figure 4.
The graph has four type of internal faces and with one external face The sum of degree of incident vertices to the external face is
The number of faces are and are given by
Now the face index of the graph is given by
After some mathematical simplifications, we can get
There are three regular plane tessellations known to exist, each constituted from the same type of regular polygon: triangular, square, and hexagonal. The triangular tessellation is used to define the hexagonal network, which is extensively studied in [54]. A dimensioned hexagonal network has vertices and edges, where is the number of vertices on one side of the hexagon. It has diameter. There are six vertices of degree three that are referred to as corner vertices. Moreover, the result required detailed are available in the Table 2.
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Theorem 2.3. Let be the triangular honeycomb network of dimension Then the face index of graph is
Proof. Consider be a graph of triangular honeycomb network. The graph has one internal and only one external face while graph with contains four types of internal faces and with one external face
For the sum of degree of incident vertices to the external face is and in the sum of degree of incident vertices to the external face is Whenever the graph the sum of degree of incident vertices to the external face is Similarly, for has dimension then sum of degree of incident vertices to the external face is
The number of internal faces with degree in each dimension is given in Table 2.
By using the definition of face index we have
Hence, this is our required result.
Given carbon sheet in the Figure 6, is made by grid of hexagons. There are few types of carbon sheets are given in [55,56]. The carbon sheet is symbolize as where represents the total number of vertical hexagons and denotes the horizontal hexagons. It contain total vertices and edges. Moreover, the result required detailed are available in Tables 3 and 4.
Dimension | ||||
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Theorem 2.4. Let be the carbon sheet of dimension and Then the face index of is
Proof. Consider be the carbon sheet of dimension and Let denotes the faces of graph having degree which is and denotes the number of faces with degree A graph for a particular value of contains three types of internal faces and with one external face While for the generalize values of it contain four types of internal faces and with one external face in usual manner. For the face index of generalize nanotube, we will divide into two cases on the values of
Case 1: When has one row or
A graph for a this particular value of contains three types of internal faces and with one external face For the face index of carbon sheet, details are given in the Table 3. Now the face index of the graph is given by
Case 2: When has rows.
A graph for generalize values of contains four types of internal faces and with one external face For the face index of carbon sheet, details are given in the Table 4. Now the face index of the graph is given by
Given structure of polyhedron generalized sheet of in the Figure 7, is made by generalizing a polyhedron structure which is shown in the Figure 8. This particular structure of polyhedron are given in [57]. The polyhedron generalized sheet of is as symbolize where represents the total number of vertical polyhedrons and denotes the horizontal polyhedrons. It contain total vertices and edges. Moreover, the result required detailed are available in Tables 3 and 5.
. | . | . | . | . | . | . | . |
. | . | . | . | . | . | . | . |
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Theorem 2.5. Let be the polyhedron generalized sheet of of dimension and Then the face index of is
Proof. Consider be the polyhedron generalized sheet of of dimension and Let denotes the faces of graph having degree which is and denotes the number of faces with degree A graph for the generalize values of it contain seven types of internal faces and with one external face in usual manner. For the face index of polyhedron generalized sheet, details are given in the Table 5.
A graph for generalize values of contains seven types of internal faces and with one external face Now the face index of the graph is given by
With the advancement of technology, types of equipment and apparatuses of studying different chemical compounds are evolved. But topological descriptors or indices are still preferable and useful tools to develop numerical science of compounds. Therefore, from time to time new topological indices are introduced to study different chemical compounds deeply. In this study, we discussed a newly developed tool of some silicate type networks and generalized sheets, carbon sheet, polyhedron generalized sheet, with the face index concept. It provides numerical values of these networks based on the information of faces. It also helps to study physicochemical characteristics based on the faces of silicate networks.
M. K. Jamil conceived of the presented idea. K. Dawood developed the theory and performed the computations. M. Azeem verified the analytical methods, R. Luo investigated and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.
This work was supported by the National Science Foundation of China (11961021 and 11561019), Guangxi Natural Science Foundation (2020GXNSFAA159084), and Hechi University Research Fund for Advanced Talents (2019GCC005).
The authors declare that they have no conflicts of interest.
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