Computation of Multi-Agent Based Relative Direction

Computation of Multi-Agent Based Relative Direction Learning Specification Submitted By

S. Rayhan Kabir ID: 133-35-561 Bachelor of Science in Software Engineering Department of Software Engineering Daffodil International University [email protected]

Supervised by Dr. Shaikh Muhammad Allayear Associate Professor Department of Software Engineering Associate Professor and Head Department of Multimedia & Creative Technology Daffodil International University

Submitted Date: September 2017

Daffodil International University Dhaka, Bangladesh

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DECLARATION It hereby announces that, this bachelor thesis under the supervision of Dr. Shaikh Muhammad Allayear, Associate Professor, Department of Software Engineering, Daffodil International University. It is also declared that neither this thesis nor any part of this has been submitted elsewhere for award of any degree.

Submitted by:

……………………………………… S. Rayhan Kabir ID: 133-35-561 Batch: 12th Department of Software Engineering Daffodil International University Faculty of Science & Information Technology Daffodil International University

Certified by:

……………………………………… Dr. Shaikh Muhammad Allayear Associate Professor Department of Software Engineering Associate Professor and Head Department of Multimedia & Creative Technology Faculty of Science & Information Technology Daffodil International University

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APPROVAL This bachelor thesis titled “Computation of Multi-Agent Based Relative Direction Learning Specification”, submitted by S. Rayhan Kabir, ID: 133-35-561 to the Department of Software Engineering, Daffodil International University has been accepted as satisfactory for the partial fulfillment of the requirements for the degree of B.Sc. in Software Engineering (SWE) and approved as to its style and contents.

BOARD OF EXAMINERS

----------------------------------------------Dr. TouhidBhuiyan Professor and Head Department of Software Engineering Faculty of Science and Information Technology Daffodil International University

Chairman

----------------------------------------------Dr.Md. Asraf Ali Associate Professor Department of Software Engineering Faculty of Science and Information Technology Daffodil International University

Internal Examiner 1

----------------------------------------------Manan Binth Taj Noor Lecturer Department of Software Engineering Faculty of Science and Information Technology Daffodil International University

Internal Examiner 2

----------------------------------------------Dr. Md. Nasim Akhtar Professor and Chairman Department of Computer Science Engineering Dhaka University of Engineering & Technology, Gazipur

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External Examiner

Abstract The most widely recognized relative directions are left, right, up, down, backward and forward. This research paper presents another algorithm for computing the relative directions between two agents, where one agent can learn another agent’s relative directions. We exhibit a study contrasting direction construct guidelines and relative direction instructions with respect to people on foot in a genuine city condition, measuring both goal and subjective achievement. Eyewitnesses commonly depict their condition by determining the relative directions in which they see different items or other individuals from their perspective. Be that as it may, it is surprisingly difficult to integrate relative directions got from various observers between two agents. In this paper, we introduce a novel subjective portrayal and representation of this MultiAgent Relative Direction (MARD) algorithm can solve these problems. For handling and recognizing relative direction, its executed work method or computation sets aside more opportunity for the result. Its actualized computation has few stages for distinguishing relative direction. Endeavor to diminish one stage and expected the outcome will be quicker.

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Acknowledgments Firstly, I might want to thank my supervisor, Associate Professor Dr. Shaikh Muhammad Allayear. I owe such a great amount to his motivating direction over the span of this venture, for his recommendations on papers to peruse, and for his endless hours of accommodating exchanges and assessment. He gives me an opportunity to work in Smart Data Science Center (SDSC) for complete my research. SDSC is a computer research laboratory of Daffodil International University. I might likewise want to demonstrate appreciation to my committee, including Associate Professor Dr. Md. Asraf Ali, Chairman, Project/Thesis Committee, Department of Software Engineering and my noteworthy our department head Professor Dr.Touhid Bhuiyan for their profitable instructions. All the more by and large, I can't exaggerate the amount Daffodil International University's software engineering offices have helped me develop as an understudy. Uncommon much gratitude goes Assistant Professor Imran Mahmud for putting me on the way to seeking after hypothetical software engineering research and for being a uniquely rousing coach and to lecturer Ms. Manan Binth Taj Noor for filling in as my scholarly consultant. I might likewise want to thank lecturer Ms. Fouzia Rahman for teaching one incredible course that truly got me amped up for a few complex factors. I would also like to thank lecturer Ms. Rubaida Easmin for her incredibly extensive and important input on the evidence of my principle result. I'm particularly appreciative for Research Associate MD. Tahsir Ahmed Munna and lecturer Mirza Mohtashim Alam for being regular teammates on issue sets. I'm particularly thankful for my paper observers Assistant Professor K. M. Imtiaz-Ud-Din and lecturer Md. Anwar Hossen for being continuous collaborators on issue sets and their suggestions on research report to peruse or read. Some extended and customized parts of this thesis recently got an opportunity to present at International Conference on Intelligent Sustainable Systems (ICISS 2017), in India and publisher is IEEE Xplore [35]. There I have described how intelligent computer learn and identify human's relative directions which based on this thesis dissertation. I hope I can successfully showcase my research in this conference. Lastly, I might want to thank my parents for bringing me into this world and making everything conceivable. They were the reason I initially began to look all starry eyed at learning, and I am appreciative consistently for what they have done to raise me up to be simply the best form.

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Table of Contents Abstract ......................................................................................................................................... III Acknowledgments......................................................................................................................... IV Introduction ..................................................................................................................................... 1 1.1 Overview ........................................................................................................................... 1 1.2 Research Objectives .......................................................................................................... 2 1.3 Research Questions............................................................................................................ 2 1.4 Organization ...................................................................................................................... 3 1.5 Definitions ......................................................................................................................... 4 1.6 Motivation of Research ..................................................................................................... 4 Background and Literature Review ................................................................................................ 6 2.1 Relative Direction Fundamentals ...................................................................................... 6 2.2 Multi-Agent System Environment..................................................................................... 7 2.3 Previous Research and Work ............................................................................................. 8 2.4 Left-Right Confusion ....................................................................................................... 13 2.4.1 Research about Left-Right Confusion ........................................................................ 14 2.4.2 Artificial Intelligence Perspective .............................................................................. 14 Proposed Algorithm Model........................................................................................................... 18 3.1 Induction .......................................................................................................................... 18 3.2 Tracking Agent and Handoff Agent ................................................................................ 19 3.2.1 Direction Points .......................................................................................................... 19 3.2.2 Various 3D Aspects .................................................................................................... 20 3.2.3 Realistic Inputs ........................................................................................................... 21 3.3 Mathematical Exploration ............................................................................................... 22 3.4 Structure of Algorithm..................................................................................................... 22 3.5 Identify Relative Direction .............................................................................................. 25 3.6 Machine Learning Aspect ................................................................................................ 26 3.7 Alternative MARD Algorithm Approach ........................................................................ 26 3.8 Multi-Agent Route Direction........................................................................................... 31 Methodology ................................................................................................................................. 33 4.1 Algorithm Engineering Method....................................................................................... 33 4.2 Experiment Control ......................................................................................................... 35 Results and Analysis ..................................................................................................................... 36 5.1 Accuracy Result ............................................................................................................... 36 5.2 Comparison ...................................................................................................................... 40 5.2.1 Comparison with UAV Relative Attitude Estimation Algorithm .............................. 40 5.2.2 Comparison with Others Algorithms .......................................................................... 42 Discussion ..................................................................................................................................... 44 6.1 Summary.......................................................................................................................... 44 6.2 Conclusion ....................................................................................................................... 45 6.3 Future Work ..................................................................................................................... 46 Bibliography ................................................................................................................................. 47

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List of Tables

Table 1: Relative directions and their numerical values. .................................................... 21 Table 2: Condition of Cases. ............................................................................................... 27 Table 3: Order of Cases. ..................................................................................................... 28 Table 4: Accuracy result ..................................................................................................... 39 Table 5: Comparison of case solution between two algorithms. ........................................ 40 Table 6: Multi-agent based and single agent based algorithm. ........................................... 43

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List of Figures

Figure 1: Different types of relative directions. ................................................................ 7 Figure 2: Characteristics of Multi-agent System Environment. ........................................ 8 Figure 3: Tracking UAV and Handoff UAV .................................................................... 9 Figure 4: Relative directions (right and left) of two agents ............................................. 15 Figure 5: Agent A ordered agent R to find the mobile object ................................... 16 Figure 6: Agent A calculated that his right direction is equal to agent .......................... 16 Figure 7: Agent A ordered agent R ................................................................................. 17 Figure 8: Direction points of Handoff Agent and Tracking Agent. ................................ 19 Figure 9: Different cases of tracking agent in 3D aspect. ............................................... 20 Figure 10: Tracking agent's direction identification ........................................................ 25 Figure 11: Multi-agent based route direction in mapping. .............................................. 32 Figure 12: Algorithm Engineering methodology cycle. .................................................. 34 Figure 13: Comparison graph of case solution between two algorithms. ....................... 41 Figure 14: Use of area between two algorithms. ............................................................. 41

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List of Algorithms

Algorithm 1: Multi-Agent Relative Direction Algorithm .................................................. 24 Algorithm 2: Direction Identification Algorithm ............................................................... 26 Algorithm 3: Alternative MARD Algorithm ...................................................................... 30

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Chapter

1

Introduction The most generally perceived relative directions are left, right, up, down, backward and forward. A man giving relative directions will utilize center terms. A multi-agent framework is an automated framework made out of different associating keen operators inside a domain. Multiagent frameworks can be utilized to take care of issues that are troublesome or inconceivable for an individual agent or a solid framework to understand. This paper explores another computation for assessing the relative direction between two agents.

1.1

Overview

Learning and identify relative directions in the multi-agent based system depends on three pairs of relative directions which are forward and backward, left and right, up and down. The experiment or research of multi-agent based relative direction learning the algorithmic process for dealing with computational issues, while one agent wants to learn another agent's relative directions. In here we show a new algorithm for processing the relative directions between two agents, how one agent can learn another agent’s relative headings in 3D perspective. In this paper, we introduce about Multi-Agent Relative Direction (MARD) algorithm concept which can represent this issue. Since the disclosure of "Plaid Motion Coherence on Component Grating Directions" by Jeounghoon Kim and Hugh R.Wilson in 1993 and its understanding unmistakably demonstrates that coherence of movement for 2D designs in various spatial scales depends basically on the relative direction of movement of part gratings [13]. Intellectual mapping research has generally centered on how people explore and procure spatial data about genuine situations. An old research which presents two examinations that analyze how people learn relative direction between landmarks in a desktop virtual condition (William S. Albert, Ronald A. Rensink and

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Jack M. Beusmans, 1999). This relative direction test included showing the course of concealed landmarks from various vantage focuses in nature [14]. In 2017 a research which researchers are Attiya Mahmood, Jon W. Wallace, and Michael A. Jensen. They reveal estimation that, given any announcement in Unmanned Aerial Vehicles (UAV), where estimation of relative attitude between two unmanned flying vehicles which in view of different information and various yield radio recurrence transmissions between the two flying machine. Specialist demonstrated that three Euler points required depicting the relative attitude [1]. Yet, computer researchers are regularly concerned about settings where agents are asset limited, in which case a few questions remain: does there exist a multi-agent system condition, given any announcement relative direction logic, either creates a short verification or infers that no short evidence exists? Comparably, it approaches whether the class of issues for which one can rapidly check a proposed arrangement is the same as the class of issues for which one can rapidly discover such an answer. This absence of advance may urge to look for new algorithmic procedures for identify or learning relative directions for multi-agent based system. As we will find in this thesis, specification of learning total six relative directions between two the agents and identify relative directions by using of two directions.

1.2

Research Objectives

The main objective of this thesis is how agents are identify or learn relative directions among them or each one another by using our propose MARD algorithm where MARD algorithm performs from multi-agent viewpoints. This research report exhibits about three different algorithms. This research report exhibits about three different algorithms. The purpose of first algorithm is learn relative direction between two agents, purpose of second algorithm is identify a agent and also detect relative direction and purpose of third algorithm is how one agent learn another agent's relative direction by using of two direction value. All thought of these propose algorithm approach show with different issues are additionally talked about in chapter 3 (Proposed Algorithm Model).

1.3

Research Questions

The thesis with titles showing that how relative directions are works between two agents from computer aspects. There have exactly some research questions and this will enable to understand some features of this thesis.

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     

1.4

Why need relative direction and MARD algorithm? What direction learning opportunities between two agents in computer science aspects? How relative direction works in 3D environment and route direction? Is it a new or modify approach? Why use numerical value in the approach? Why numerical values have been used for indicate relative direction?

Organization

In Chapter 1, we revolve our work around the thesis and quickly present about foundation overview and purpose of this algorithmic research. In Chapter 2, we concentrate on a particular issue for building algorithm. In here we demonstrate which literature is audited. We at that point exhibit the evidence of our motivation, which utilizes relative direction of agents, before showing our own confirmation of a similar outcome and our outcome on endless groups of limit parts. This chapter briefly foundation or background of thesis, past research of relative direction, artificial intelligence based concept. In Chapter 3, we introduced about MARD algorithm. In here we have also displayed various 3D cases of agent and another different approach of MARD algorithm which will helps in machine learning sector. We also demonstrate multi-agent route directions which present interpretations mapping over the different area. In Chapter 4, we talk about our research methodology and exploration technique. This part shows research design and Algorithm engineering method for this algorithmic research. In Chapter 5, we analyse our algorithm, result. This part demonstrates explore examination and execution assessment with time and histogram. Different type of solving strategy analysis we exhibit in this section. In Chapter 6, we endeavor to settle on a choice about legitimacy of relative direction algorithm for multi-agent environment. This part indicates summary of this thesis and finishing. In Bibliography, we try to show proper references which help for complete this research. In Appendix, we incorporate audits of the fundamental many-sided quality classes, the portrayal of code, programming structure and evidences of some minor subtle elements specified in the body of the thesis.

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1.5

Definitions

MARD algorithm: Multi-agent relative direction (MARD) algorithm refers an algorithm strategy which identify or learning relative direction among the agents where one agent can be learning other agent’s relative directions. UAV: Unmanned aerial vehicle (UAV) normally acquainted as a drone. It is an airplane or aircraft without any human pilot on board. UAVs are a segment of an unmanned flying machine which incorporates a UAV, a ground-based controller, and an arrangement of interchanges between the two. The flight of UAVs may work with different degrees of self-rule: either according to remote control by a human administrator or independently by locally available computers. Multi-agent: A multi-agent framework or system is an automated manner made out of various associating smart operators inside a domain. Multi-agent system can be utilized to take care of issues that are troublesome or incomprehensible for an individual operator or a solid framework to solve. Intelligence may involve some methodic, utilitarian, procedural approach, algorithmic inquiry. Despite the fact that there is impressive cover, a multi-agent process is not generally the same as an agent-based model. Route Directions: A route is regularly part into a few fragments that are then verbalized. These verbalized directions can be guidelines to make a specific move, for example, "walk" or "turn", or portrayals of the map. Direction of Arrival: In signal technology writing, direction of arrival (DOA) means the course from which as a rule an engendering wave touches base at a point, where as a rule an arrangement of sensors are found. DOA discovers the direction in relative to the cluster where the sound source is found. OPRA: The Oriented Point Relation Algebra (OPRA) distributes for subjective spatial description and reasoning. OPRA is an introduction calculus math with movable granularity. OPRA depends on objects which are spoken to as oriented points. Oriented points are indicated as match of a point and a direction on the 2D-plane.

1.6

Motivation of Research

The ―Direction Learning‖ has been a well known research point among specialists and researchers of arithmetic and computer science. With the objective of limiting the relative direction of the agent in estimating agent's relative direction problem. Arrangements of this issue can be connected to an extensive variety of improvement issues.

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Estimation of relative attitude between two UAV [1] is the latest research of direction learning calculation which can play numerous info various yield radio recurrence transmissions between the two aircraft. Most likely this recent research initially indicates multi-operator idea based relative state of attitude learning calculation. Our analysis is to attempt to give some calculation which takes after a few exercises of this recent research. Generally this recent research motivated us to doing our research. The structures we are searching for are just thickness varieties in the computation. Contrasting landmark based guidelines and relative direction directions on people on foot in a genuine city condition, measuring both goal and subjective achievement [4]. We find that at some choice focuses, multi agent based relative direction algorithm work better for route direction in mapping. We show a strategy that how our research gives better instruction in different computer science area.

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Chapter

2

Background and Literature Review The background gives a prologue to Multi-Agent Relative Direction (MARD) algorithm. It additionally presents some artificial intelligence (AI) perspective concept about this algorithm which is of intrigue while talking about multi-agent based relative direction learning process. This section describes the sequencing relative direction in any multi-agent based issue. Highlights of an algorithm are examined with a specific end goal to order the issue into subissues like object finding problem, structure issue. This Chapter additionally gives a presentation of the unmanned aerial vehicles and talks about the similarities and contrasts between past research and this thesis.

2.1

Relative Direction Fundamentals

The most well-known relative directions are right, left, up, down, forward and backward. There are definite connections between the relative directions. Forward-backward, left-right, and updown are three sets of integral relative directions. Relative directions are otherwise called egocentric coordinates. Relative directions can be helpful to individuals who are new to the area of cardinal directions. Since meanings of left and right in view of the geometry of the natural habitat are inconvenient. The importance of relative direction words is passed on through custom, cultural assimilation, training, and direct reference. One normal meaning of up and down utilizes gravity and the globe as an edge of reference. Up is then characterized as the other way of down. Another normal definition utilizes a human body, standing upright, as an edge of reference. Forward and backward might be characterized by alluding to a question's or individual's movement. Forward is characterized as the bearing in which the question is moving. Backward is then characterized as the other way to forward. Then again, forward might be the direction pointed by the onlooker's nose, characterizing backward as the heading from the nose to the sagittal fringe in the eyewitness skull. Concerning a ship forward would show the relative 6 © 2017 by Daffodil International University

position of any protest lying toward the path the ship is pointing. In Figure 1 illustrates different relative directions from human perspective.

Figure 1: Different types of relative directions.

For symmetrical purpose, it is additionally important to characterize forward and backward regarding expected course. Many mass travel trains are constructed symmetrically with matched control corners, and meanings of forward, in reverse, left, and right are brief.

2.2

Multi-Agent System Environment

Multi-agent structure is a computerized way made out of different partner keen administrators inside a space. Multi-specialist framework can be used to deal with issues that are troublesome or inconceivable for an individual agent or a strong system to solve. Multi-agent frameworks comprise of agents and their condition. Regularly multi-agent frameworks examine alludes to programming operators. Nonetheless, the specialists in a multi-agent system could similarly well

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be robots, people or human groups. A multi-specialist framework may contain consolidated human-agent groups. The inspiration for considering multi-agent system regularly originates from enthusiasm for programming or software agents. The material traverses teaches as assorted as software engineering (artificial intelligence, hypothesis, and distributed computing), financial matters (essentially microeconomics concept), research, scientific rationality, and phonetics. In understanding the determination made here, it is valuable to remember the accompanying algorithms [27].

Figure 2: Characteristics of Multi-agent System Environment.

Multi-agent environment can include specialists making arrangements for a shared objective, an agent organizing the plans or arranging of others, or specialists refining their own particular designs while consulting over errands or assets. The theme likewise includes how agents can do this progressively while executing designs. Multi-agent booking varies from multi-agent planning a similar way arranging and vary in planning regularly the undertakings that should be performed are as of now chose, and by and by, planning tends to concentrate on algorithms for particular issue areas.

2.3

Previous Research and Work

To represent the idea of multi-agent relative direction (MARD) algorithm previously formally characterizing every one of the parts that go into the several approaches, we give a genuinely

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casual exploration of one of its more eminent examples of success stories. All things considered, this subsection might be skipped if the peruser likes to jump straight into definitions. 

Relative Attitude Estimation of UAV (Mahmood, Wallace and Jensen, 2017):

They propose a renewed algorithm for evaluating the relative attitude between two unmanned aerial vehicles (UAV) in view of various multiple input and output radio recurrence transmissions between the two airplanes. The strategy can evaluate each of the three Euler points required to portray the relative disposition [1].

Figure 3: Tracking UAV and Handoff UAV in Relative Attitude Estimation of UAV’s paper

In here the system show comprising of Tracking and Handoff UAVs and their comparing nearby arrange outlines and also the connection between the new organize outlines at the two UAVs framed from direction of entry estimates. Each UAV has its own particular nearby facilitate outline characterized by the unit-standard vectors where i ∈ {h, t} for Handoff and Tracking, individually. The analysts of this paper shows a novel calculation that consolidates direction of arrival (DOA) gauges with polarimetric multi-antenna apparatus channel appraisals to figure the relative attitude between two UAVs. 

Relative Direction Change (Hahn, Bethge and Döllner, 2017):

A topology-based metric for design solidness in treemaps of the Relative Direction Change (RDC) introduced metric considers the nearness and course of action of single shapes in a treemap, and takes into consideration a rotation-invariant portrayal of format alterations between two snapshots of a dataset delineated with treemaps [2].

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

Fast Contour-Tracing Algorithm Based on a Pixel-Following Method (Seo, Chae, Shim, Kim, Cheong and Han, 2016):

The experiment introduces a novel contour-tracing algorithm for quick and exact contour following. This algorithm orders the sort of contour pixel, in light of its neighborhood design. At that point, it traces the following contour utilizing the past pixel’s model. The algorithm follows shape pixels along the clockwise direction from the present pixel, i.e., it consecutively seeks nearby dark pixels of the present pixel utilizing a relative directional request, for example, left, front-left, front, front-right, right, rear right and back. To decide the contour point, which might be a contour pixel, the tracer recognizes the power of its adjoining pixel Pr and the new absolute direction dr for Pr by utilizing relative direction data r ∈ { front, front − left, left, rear − left, rear, rear − right, right, r ∈ { front − right} [3]. 

Relative Directions Work Better Than Landmarks (Götze and Boye, 2015):

People make broad utilization of landmarks while depicting the best approach to others and are more effective after directions that cover landmarks. It exhibit an examination contrasting landmark based guidelines and relative direction indication on people on foot in a genuine city environment, measuring both target and subjective achievement. Researchers find that at some choice focuses, relative direction guidelines work better. Specifically, guidelines that avoid a landmark and use just a relative direction like "left" or "right", appear to be favored at some decision focuses, especially those with a basic arrangement where streets meet at right edges [4]. 

Complexity of Reasoning with Relative Directions (Lee, 2014):

In the case of reasoning upon relative directions can be performed in NP has been an open issue in subjective spatial reasoning. Effective reasoning with relative directions is fundamental, for instance, rule-compliant agent navigation [5]. In this research reasoning upon relative directions is ∃R-complete. As a result, reasoning with relative directions is not in NP, if not NP = ∃R, where ∃R is a many-sided complexity class. 

Effective Reasoning about Relative Directions (Lee, Renz and Wolter, 2013):

Eyewitnesses commonly depict environment by indicating the relative directions in which they see different articles or other people from their perspective [6]. They demonstrate that reasoning in StarVars is in NP and present the primary algorithm that enables to viably coordinate relative direction data from various observers. They built up a spatial portrayal, StarVars, which increases cardinal direction relations to illustrate to relative directional information. 

Relative Identification and Direction for Wireless Network (Weng and Lai, 2013):

A less intricate, more productive routing algorithm called as relative identification and directionbased sensor routing (RIDSR) algorithm [7]. RIDSR influences sensor hubs to set up more

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dependable and vitality effective routing path for information transmission. RDSR calculation not just tackles the routing loop problem inside the algorithm yet in addition encourages the immediate choice of a shorter way for routing by the sensor node. Moreover, it saves energy and broadens the existence of the sensor hubs. 

Relative Direction of Oriented Points (Mossakowski and Moratz, 2012):

An imperative problem in qualitative spatial reasoning is the portrayal of relative directions. In this paper, basic geometric tenets that empower reasoning about the relative direction into oriented points [8]. This structure arranged a oriented point algebra OPRAm, has a versatile granularity m. In this paper a basic algorithm for figuring the OPRAm synthesis tables and demonstrates its rightness. 

Verbal Navigational Directions in Relative Frames (Mossakowski and Moratz, 2008):

This examination inspected how people utilize verbal route directions conferred in relative and absolute edges of reference in genuine route, especially contrasts or likenesses in cognitional load postured by the two frames of reference [9]. This instruction took a gander at how people utilize verbal route directions offered in two sorts of frames of reference, relative and absolute, in genuine route. Specifically, inspected the distinctions or likenesses in the trouble of utilizing and preparing data given in favored and non preferred casings of reference, and whether individuals could adjust to or switch between the two frames of reference. 

Triangular Multiple Flow Direction Algorithm (Seibert and McGlynn, 2007):

Gridded digital elevation data (DEMs) frequently alluded to as DEMs, are a standout amongst the most broadly accessible types of natural information. Here a give an account of a stream routing algorithm and contrast it with three regular classes of calculations at present in across the board utilize. The upside of the algorithm is that unrealistic dispersion on planar or curved hillslopes is dodged, while numerous flow directions are permitted on raised hillslopes. The steepest directions point fairly to one left and right. Be that as it may, since there must be one outflow direction in the algorithm, just a single of these two directions gets territory, while the two directions ought to get region [10]. 

An ant colony optimisation algorithm for the 2D and 3D (Shmygelska and Hoos, 2005):

The protein folding issue is a principal issue in computerized molecular science and biochemical physics. In this work, research demonstrated that ant colony optimisation (ACO) can be connected in a somewhat straight-forward path to the 2D and 3D HP Protein Folding Problems. Despite the fact that our ACO-HPPFP-3 calculation depends on exceptionally straightforward structure parts (single relative directions) and a basic backup neighborhood seek strategy [11].

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

Relative Direction as a Binary Relation (Moratz, 2006):

A central issue in robotics is the representation of relative orientation. This paper introduces a new calculus about oriented points which has a scalable granularity [12]. In this calculus, named OPRA, simple rules can generate the minimal composition. Furthermore, the algebraic closure for a set of OPRA statements is sufficient to solve knowledge integration tasks in robotics. 

Plaid Motion Coherence on Component Grating Directions (Kim and Wilson, 1993):

A few element motion directions were created little to vast angular contrasts. In here a confirmation obviously demonstrates that coherence of movement for 2D designs in various spatial scales depends fundamentally on the relative direction of motion of component gratings and is moderately autonomous of difference and speed. It is likewise free of the SF distinction between two parts as long as the proportion is more prominent than around 3:1 [13]. It would appear to be environmentally more conceivable for the visual system to decide inflexibility of movement in view of the relative directions of neighborhood motion vectors. 

Relative Directions between Landmarks (Albert, Rensink and Beusmans, 1999):

This examination presents two tests that inspect how people learn relative directions between landmarks in a desktop virtual condition. Subjects were introduced preview pictures of various virtual environments containing recognizing points of landmarks and a road network. The introduction of each virtual environment, subjects were given a relative direction test [14]. The relative direction test included demonstrating the direction of concealed landmarks from various vantage focuses in the environment. 

Robot kinematics (1998):

Kinematics is the connections between the positions, speeds, and increasing velocities of the connections of amanipulator, where a controller is an arm, finger, or leg. This exploration characterizing the arrangement as far as elbow up or down, left or right handed [15]. A matrix portrays the change from the base to the hand of the controller, a succession called the forward kinematic change of the manipulator. 

Maps and Relative Direction (Foster, 2016):

It's quite basic to portray direction in connection to area on a map. Go up that path, down here, or over yonder. Up, down, and over are relative directions given from a perspective, regularly physical topographic change [16]. Up stream, down the slope, and over to the lake. The words up and down can be held in respect to gravity. Unless people are alluding to up and down in connection to geology, or in relative to a specific area. 

Relative Main Line layout algorithm:

The Relative Main Line format algorithm works from traits that enable the calculation to distinguish the straight lines that is, the principle lines and root schematic nodes from which

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those straight lines begin. Root schematic nodes can be set utilizing the Set Schematic Root tool [17]. Set Schematic Root to determine the beginning stages of the straight lines. The algorithm initially looks nodes to observe candidates to be the root node that is, node associated with a single link that can be considered as the beginning point for a straight line. 

Dragonfly Algorithm for Solving Multi-objective Problems (Mirjalili, 2016):

Dragonfly calculation is a fiction swarm intelligence streamlining technics. The progression vector of this algorithm demonstrates the direction of the development of the dragonflies and characterized as takes after: Xr + 1 = ( sSi + aAi + cCi + fFi + eEi ) + wXt where s demonstrates the division weight, Si shows the partition of the i-th individual, a is the arrangement weight, Ai is the arrangement of i-th singular, c shows the attachment weight, Ci is the union of the i-th singular, f is the sustenance factor, Fi is the nourishment wellspring of the ith singular, e is the foe factor, Ei is the position of foe of the i-th singular, w is the dormancy weight, and t is the cycle counter [18]. 

DOA Estimation of Animal Vocalizations (Hedley, Huang and Yao, 2017):

A recording system built from two Wildlife Acoustics SM3 recording units that can calculate the direction-of-arrival (DOA) of an approaching signal with high precision [20]. Signal processing algorithms, similar to the MUSIC algorithm utilized their analysis, they utilize these stage contrasts to decide the angle from which each sound arrived (α and β for the red and blue winged creatures, individually, relative to a self- assertive mention angle, marked 0). The system utilizes four all the while recording receivers to evaluate the direction from which a sound arrived, in view of the stage contrasts of the approaching sound waves at the microphones. 

Discrete-State-Based Vision Navigation Control Algorithm (Wei, 2015):

To set out a principled dialog of the exactness and productivity of navigation algorithms, entirely quantitative meanings of following error, actuator impact, and time proficiency are built up. The navigation algorithm would control the robot following the particular direction [21]. In the wake of characterizing the relative angle between desired velocity vector and the real velocity vector as course blunder signified by e. Most extreme Steering Angle max. This value is the practical steering angle pushing forward in a direction. That implies the robot can move forward with the direction scope of [−max, max]. Negative esteem implies the robot turns right while the positive value implies it turns left with respect to the robot.

2.4

Left-Right Confusion

Left-right confusion is the inconvenience a few people have in recognizing the distinction between the headings left and right. These individuals can as a rule typically perform every day

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exercises, for example, driving as indicated by signs and exploring as indicated by a map, however will regularly go astray when advised to turn left or right and may experience issues performing activities that require exact comprehension of directional orders.

2.4.1 Research about Left-Right Confusion Challenges in left–right discrimination (LRD) are usually experienced in regular day to day existence circumstances. An examination demonstrates that the neurocognitive components of left– right separation and the particular part of left precise gyrus [29]. In later an examination surveyed the connection between self-appraised right–left confusability and execution on the Money Road-Map Test (MRMT). Eighty-six understudies appraised right–left subjective confusability utilizing a poll, and afterward finished the Money Road-Map Test. Another examination researches the connection between the view of bilateral symmetry and leftright bewilderment in neurologically in place grown-ups by utilizing tachistoscopic introduction of boosts and a decision response time technique. Scientists found a little yet predictable pattern toward snappier symmetry judgments in left-right distracted subjects. The legitimacy of such self-report measures in foreseeing real execution on right-left segregation undertakings is addressed since the outcomes, in any event as a component of handedness, relied upon the inquiry [30, 31, 32].

2.4.2 Artificial Intelligence Perspective Artificial intelligence (AI) is insight displayed by machines, instead of people or different creatures (natural intelligence). In software engineering, the field of AI investigate characterizes itself as the investigation of intelligent agents are any gadget that sees its condition and takes activities that expand its risk of achievement at some objective. Conversationally, the expression computerized intelligence is connected when a machine impersonates subjective capacities that humans connect with other human personalities, for example, "learning" and "critical thinking". Learning or identify direction is an exploration range in software engineering and computer science, with territories, for example, unraveling, trouble and era. One of the critical choice rule for picking MARD algorithm for this proposal have hence been the algorithm hidden strategy for crossing the hunt space, for this situation deterministic and stochastic strategies. Possible left-right confusion with artificial intelligence perspective show in Figure 4, 5, 6 and 7, each agent has its own local relative directions. Where Agent 1 = R, Agent 2 = A and Mobile phone is a object for find mobile phone object. Primary objective of these figures are to find the object and defining the left and right direction where relative directions (right and left) of two agents show from different perspective. The process for achieving this purpose is as follows: 14 © 2017 by Daffodil International University

Figure 4: Relative directions (right and left) of two agents from different perspective. In here a mobile phone is a example of object.

1) In Figure 4, there are two agent respectivelty agent A and agent R and their right and left directions different from different perspective. Mobile phone is an example of object for understanding left-right confusion in human and artificial intelligence both perpective. The Mobile object is located on its left side of agent R and on the right side of agent A. 2) In Figure 5, agent A ordered agent R to find the mobile object on the right direction. When agent R searches the mobile phone object in his right direction, he could not find any mobile phone, because relative directions between two agents are different. It means that agent A’s right direction is not equal to agent R’s right direction. Relative directions between two agents are different. 3) In Figure 6, agent A think and calculated that his right direction is equal to agent R’s left direction. Agent A also calculated that his left direction is equal to agent R’s right direction. At last agent A learn that Agent R’s relative directions. 4) Lastly in Figure 7, agent A ordered agent R to find a mobile phone on agent R’s left direction and agent R find the mobile phone object.

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Figure 5: Agent A ordered agent R to find the mobile object on the right direction but agent R could not find any mobile phone on his right direction, because relative directions between two agents are different.

Figure 6: Agent A calculated that his right direction is equal to agent R’s left direction and left direction is equal to agent R’s right direction.

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Figure 7: Agent A ordered agent R to find a mobile phone on agent R’s left direction. At last agent R find the mobile object.

Basically our MARD algorithm will work like this way. As a result one agent easily can understand anodher agen’s left-right or relative directions. Through this concept we can devlope our new multi-agent relative direction (MARD) algorithm and with the help of this algorithm we can solve left-right confusion problem in compuer or artificial intelligence perspective.

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Chapter

3

Proposed Algorithm Model To represent the idea of multi-agent relative direction (MARD) algorithm previously formally characterizing every one of the parts that go into the approach, we give a genuinely casual exploration of one of its more eminent examples of success stories. All things considered, this subsection might be skipped if the peruser likes to jump straight into definitions. In this chapter, we present the fundamental theory of MARD algorithm. We work through an illustrative case in this segment before formalizing the model. At that point we talk about the characterizing numerical properties of MARD algorithm and give a unique re-detailing of these properties that will demonstrate basic to demonstrating our primary hypothesis. At long last, we give a programming structure of MARD algorithm as far as spinor assortments and drawing ideas.

3.1

Induction

This exploration section introduces propose algorithm for figuring the relative directions between two agents, where how one agent can take in another agent's relative directions through in computer or programming perspective. We display an examination of relative direction. In this section demonstrate our experiment design, realistic input, mathematical concept and structure of MARD algorithm. We present a novel subjective depiction and portrayal this MARD algorithm can tackle these issues for dealing with and perceiving relative directions, its executed work strategy or computation puts aside greater open door for result. In order to MARD algorithm experiment, we focus on Algorithm Engineering technic. Algorithm engineering revolves around the outline, examination, analysis, implementation, optimization and exploratory appraisal of algorithms. One significant though all too every now and again overlooked issue when driving analyses in Computer Science is to ensure MARD algorithm.

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3.2

Tracking Agent and Handoff Agent

With past work focusing on attitude estimation, an algorithm using Tracking UAV and Handoff UAV for estimating relative attitude between two unmanned aerial vehicles (UAV) [1]. In the MARD algorithm we use ―Tracking Agent‖ and ―Handoff Agent‖ (see Figure 8). We consider in the algorithm where one tracking agent is following an objective on the ground, and it is wanted to have a moment handoff agent have the capacity to learning the relative direction of the tracking agent.

3.2.1 Direction Points We use total six riletive directions in our algorithm. Each agent has 6 direction points for handoff agent direction points are a, b, c, d, e, f and for tracking agent direction points are same level but reverse for that direction points are a1, b1, c1, d1, e1, f1. Firstly handoff agent knows his relative directions but in the beginning handoff agent not knows about tracking agent’s relative directions, which has been displayed in Figure 8. c1

e1

a1

b1

Tracking Agent f1 d1 c e Forward

Up

a

b Right

Left

Backward f

Down

Handoff Agent

d

Figure 8: Direction points of Handoff Agent and Tracking Agent.

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3.2.2 Various 3D Aspects 3D PC illustrations are regularly alluded to as 3D models. The view of relative directions seems from different 3D aspects. In Figure 9 we show 24 different cases of relative direction aspects of tracking agent perspective. c1 Up

c1 Up

Forward e1

b1

a1

Left

Right

Right

f1 Backward

f1 Forward

Down d1

Case 1

Backward c1 Up b1

b1

Right e1 a1

Forward c1 Down b1

Down f1 f1 d1 Left Left Forward Case 7 Case 6 c1 Forward Left b1 f1 Up

Down e1 a1 Right

Backward d1

Case 11

a1

b1

Left

Right

Down d1

Case 2

Down Forward c1

Backward e1

Right e1 a1 Up

b1 f1

Up

f1

Case 12

f1 Forward Down d1 Case 9

Forward Left c1 e1

Backward e1 a1 Right Up d1

Up

a1

b1

Down

f1 Right Case 5

Forward Up c1 e1 Right a1 Left b1

Left e1 a1 Up

Backward Right d1 Case 8

a1 Left

f1 Forward Case 4

Down

Backward

Backward Up c1 e1 Left

a1 Right b1 f1 Backward d1 Down Forward d1 Case 10

Down

a1

c1 c1 Backward Right f1 Forward Right e1 e1 Down a1 Up a1

b1

Up

b1

Backward Down c1 e1 Right b1

Left

Up d1

Backward c1

Down b1

a1 Left

f1 Backward Case 3

c1

c1 Forward Left e1

Right f1 Forward Backward d1 d1 Case 13

f1 Forward Case 14

Up Left d1

Up Up c1 Right c1 Right Left e1 e1 e1 Up Forward a1 a1 a1 Backward a1 b1 Forward b1 Backward b1 b1 Forward Backward b1 Down d1 f1 Up f1 f1 f1 f1 d1 Forward Right Right Down Left Right Left d1 Down Up d1 d1 Case 16 Case 17 Case 18 Case 19 Case 20 c1 c1 Down Left c1 Right c1 Down e1 Up Right Up Left e1 e1 e1 Backward a1 Forward a1 Backward a1 Forward a1 b1 Forward Backward b1 Forward b1 b1 Backward f1 Right Left Up f1 f1 Left Up f1 Right d1 Down d1 Down d1 Case 21 Case 22 Case 23 Case 24 d1 Left Backward c1 e1

Down Down Left c1 c1 e1 Forward a1 Backward

b1 Down f1 d1 Backward Left Case 15

Figure 9: Different cases of tracking agent in 3D aspect.

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From Figure 8 and Figure 9 we can say that relative direction of tracking agent always changeable but direction points of tracking agent are constant. Here we find total twenty four relative direction movement. So we can say that, Total relative direction movement (M) = Number of relative direction (n) * 4 Or, M = 6*4 = 24

3.2.3 Realistic Inputs At first handoff agent knows his relative directions but handoff agent not knows about tracking agent’s relative directions. Moreover handoff agent compare with tracking agent’s relative directions by tracking agent’s directions points. For MARD algorithm development we use some niumerical value for identify relative directions. Every relative direction point has own value which depends on the relative directions such as 0 for Right, 1 for Left, 2 for Up, 3 for Down, 4 for Forward and 5 for Backward, which have been shown in Table 1. We have used these values because the reason in our proposed algorithm we have used two array agent which contains these direction variables.

Relative Direction

Direction Point Value (For relative direction)

Right

0

Left

1

Up

2

Down

3

Forward

4

Backward

5

Table 1: Relative directions and their numerical values.

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3.3

Mathematical Exploration

In this area, we will numerically define the MARD algorithm with every one of the imperatives specified. The main navigating auto in the computer science serves an arrangement of benefit arrange from various wellsprings of the algorithm design. The procedure for mathematical computation of this algorithm is as follow:

Ti 

( Hj )

In here T refers tracking agent, where T = {Right, Left, Up, Down, Forward, Backward} and H refers handoff agent, where H = {right, left, up, down, forward, backward}. Besides i is the set of index T’s relative direction, where i = 0, 1, 2, 3, 4, 5 and j is the set of index H’s relative direction, where j = 0, 1, 2, 3, 4, 5. For i, handoff agent H’s j relative direction is assigned into tracking-human T’s i relative direction.

3.4

Structure of Algorithm

This investigation part presents propose algorithm for figuring the relative directions between two agents, where how one agent's can learn in another agent's relative directions through in programming point of view. In this segment exhibit our analysis plan, practical information, programming idea and structure of MARD algorithm for application. We very briefly develop MARD algorithm. The process of MARD algorithm for programming or application perspective goal is as per the following: 1) Step1: Creat a HandoffAgent class    

An array Direction = [right, left, up, down, forward, backward]. Direction point variables of handoff agent are a, b, c, d, e and f. Points are containing direction values (see Figure 8 and Table 1). So that a = 0, b = 1, c = 2, d = 3, e = 4 and f = 5. Direction point variables are involving relative directions. So that, right = a, left = b, c = up, d = down, e = forward and f = backward (see Figure 8 and Table 1).

2) Step2: Creat a TrackingAgent class  

Direction = [Right, Left, Up, Down, Forward, Backward]. Direction point variables of tracking agent are a1, b1, c1, d1, e1 and f1. Direction points contain different direction value. These values are 0, 1, 2, 3, 4 or 5 (see Figure 8, Figure 9 and Table 1).

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3) Step3: Creat a Main class, main function and object  

handoffAgent is a object of HandoffAgent class trackingAgent is a object of HandoffAgent class

4) Step4: Creat a Loop    

A variable i = 0 to 5. The condition of the loop is i