Tips & Tricks
Your SlideShare is downloading.
Like this document? Why not share!
Sharing experiences over the time-1
by Earthsoft Foundat...
Donation school bags to children of...
by Earthsoft Foundat...
Étude Démocratie & Politique
Observations about monks and social...
by Earthsoft Foundat...
10 tips to help you manage your mon...
by Chartered Account...
Reports-Sessions at Oswal Bandhu by...
by Earthsoft Foundat...
Email sent successfully!
Show related SlideShares at end
Jan 11, 2014
Comment goes here.
12 hours ago
Are you sure you want to
Your message goes here
Be the first to comment
Be the first to like this
Number of Embeds
Flagged as inappropriate
Flag as inappropriate
No notes for slide
Transcript of "50120130406043"
1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 6, November - December (2013), pp. 394-402 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME ARTIFICIAL INTELLIGENCE IN THE MOVEMENT OF MOBILE AGENT (ROBOTIC) Sreekanth Reddy Kallem AMR Institute of Technology, Adilabad, JNT University, Hyderabad, India, 504001 ABSTRACT Many difficult problems, like: Travelling Salesman Problem (TSP), Evolutionary algorithms and neural networks are required to solving computational intelligence. In these problems the travelling salesman problem TSP has large application area. Maximum benefits TSP, price collecting TSP have a large number of economic applications. TSP is also used in the transport logic raja, 2012. In this paper a mobile Robotic agent’s Movement will be discussed, using neural networks for the TSP solving, and from the TSP results finding Moving direction of the agent’s working area. This paper mainly focused on the moving direction and finding objects of the robotic agent by implementing the neural networks. Keywords: Movement of Robotic; Travelling Salesman Problem; Neural Networks; Path Planning. I. INTRODUCTION Artificial intelligence (AI) is the study of how to make computers do things which, at the moment, people do better . Artificial Intelligence is defined by the Alan Turing is “If a machine acts as intelligently as a human being then it is as intelligent as a human being.” . Thus Strong AI claims that in near future we will be surrounded by such kinds of machine which can completely works like human being and machine could have human level intelligence. One intention of this article is to excite a broader AI audience about abstract algorithmic information theory concepts, and conversely to inform theorists about exciting applications to AI. The science of Artificial Intelligence (AI) might be defined as the construction of intelligent systems and their analysis. AI is a wellestablished scientific domain that has its own history and identity. The term ‘Artificial Intelligence’ was invented by John McCarthy (1956, 1958) as the science and engineering of making intelligent machines . For the best results and real – life applications, however, artificial neural networks need to be implemented as analog, digital, or hybrid (analog / digital) hardware. Moreover, neural network processors rather than digital computer simulations seem to be the key ingredient to further expansion and commercialization of neural network technology. 394
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Robotic mobile agents have a wide range of applicability in different areas of real life. Wang, 2005; Kim, 2003; Chirillo, 2012; and Leitao, 2012. A large number of scientists are working on finding new solutions for different subsections of mobile agent applications such as navigation, localization, optimal path planning, object detection, movement of the robot. In our research, mobile agent is considered as a mobile robot. In this paper we focus on the movement of mobile agent computational intelligence and neural networks. Neural network or artificial neural network plays a major role in movement of a mobile agent (Robot). Robotic is the intelligent connection of perception to action . Our focus on discussing solution for: • Finding the movement and direction of robot • Input-output mapping performed in the movement of mobile agent • Solution for TSP in the movement of the mobile agent by using artificial neural network II. RELATED WORK Robotic agents are ideal mobile robots, ie they can perform unconstrained movements in the plane. In this paper we focus on how the mobile agent will get the direction to move from one location to another location based on the neural network. How and where the mobile agent will store the information which is given by the user and how it will recall that information. How the controller can be process the information which is given by the user. In this the mobile agent is consider as Robot. A wide variety of mobile agents equipped with different kinds of sensor types are used. Almost all of these are mobile robots of one form or another and include wheeled mobile robots, legged robots, and flying robots. There are different types of mobile agents will take different types movements, some mobile agents will take movement in the water, some are move in the sky, some other take movement on the surface. A related paper is  which discusses about robot path planning optimization of mobile agent navigation, but not discuss about the movement of the mobile agent (Robot). In this the robotic mobile agent uses an unsupervised neural network can be used for solving the TSP problem. There are different types of methods were developed for solving TSP are genetic algorithms Hui,2012, enumeration, branches and bounds, efficient algorithm of Clark and Wright, ant colony optimization Zhao, 2011; Pintea, 2007. There are also several artificial intelligence based solutions for solving the TSP and in mobile agents path planning such as genetic algorithms Kao-Thing, 2007; Nouara, 2011, solutions based on artificial recurrent networks Yogita,2012, etc. The intelligence of the robotic agent is considered based on the use of a computational intelligence algorithm used for the optimization of the path planning. The main aim of the movement of the mobile agents is transportation. A mobile robot which operates in human environments to carry out different tasks, such as transporting waste in hospitals, Transporting heavy objects or escorting people in exhibitions  . III. NEURAL NETWORKS The Artificial neural networks (ANN) or a neural network plays a major role in the movement of mobile agents. A neural network is massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experiential knowledge and making it available for use. ANN has a quicker response and higher performance compared to sequential digital computer. ANN system is composed of operators interconnected via one-way signal flow channels . A case point, ANN generalization: not only the mathematically founded learning algorithms (e.g., backpropagation) but also the surprisingly good emergent noise-immunity 395
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME of classifier networks have allowed ANN technology to move into commercial optical character recognition (OCR) products . It resembles the human brain in two respects: 1. Knowledge is acquired by the network from its environment through a learning process. 2. Interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge. Neural networks it is also called as artificial neural networks to be more precise, represent a technology that is rooted in many disciplines: neuroscience, statistic, computer science, and engineering. It fined applications in such direction as pattern recognition, signal processing, modeling. An important property: the ability to learn from input data with or without teacher. Neural networks behave like the human brain, but the human brain computes in an entirely different way from the conventional digital computer. The brain is highly complex, nonlinear, parallel computer (information processing system). Neural networks perform useful computations through a process of learning and the procedure used to perform the learning process is called learning algorithm. A. Nervous system of a Human brain A human brain consists of approximately 10ଵଵ computing elements called neurons. They communicate through a connection network of axons and synapses having a density of approximately 10ସ synapses per neuron. Neuron is the elementary nerve cell, it is the fundamental building block of the biological neural network. The nervous system of a human may be depicted as three stage system, as depicted in the block diagram (Arbib,1987). Human brain is central to the system, represented by the neural (nerve) net, which continually receives information, perceives it, and makes appropriate decisions. There are two sets of arrows are shown in the figure. Those pointing from left to right indicate the forward transmission of information-bearing signals through the system. The arrows pointing from right to left signify the presence of feedback in the system. The receptor converts stimuli from the human body or the external environment into electrical impulses that convey information to the neural net (brain) .The effectors convert electrical impulses generated by the neural net into discernible responses as system outputs. Synapses are elementary structural and functional units that mediate the interaction between neurons. Stimulus Response Receptors Neural net Effectors Fig.1: Block diagram representation of nervous system B. Network Architecture of the Neurons The Neurons of a neural network are structured is intimately linked with the learning algorithm used to train the network. We may therefore speak of learning algorithms (rules) used in the design of neural networks as being structured. We may identify three fundamentally different classes of network architectures. 396
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME 1) Single – Layer Acyclic Networks The neurons are organized in the form of layers in a layered neural network. In the simplest form of a layered network, we have an input layer of source nodes that projects onto an output layer of neurons (computation nodes), but not vice versa. In other words, this network is strictly a feed forward or acyclic type. From the fig. 2 for the case of four nodes in both the input and output layers, this network Input Layer of Source nodes Output Layer of Source nodes Fig. 2: Acyclic or Feedforward network with a single layer of neurons is called single-layer network and single layer referring to the output layer of computation nodes (neurons). We don’t count the input layer of source nodes because no computation is performed. 2) Multilayer Feedforward Network The second class of a feedforward neural network distinguishes itself by the presence of one or more hidden layers, whose computation nodes are correspondingly called hidden neurons of hidden. The function of hidden neurons is to intervene between the external input and the network output in some useful manner. By adding one or more hidden layers, the network is enabled to extract higher-order statistics. In a rather loose sense the network acquires a global perspective despite its local connectivity due to the extra set of synaptic connections and the extra dimension of neural interactions (Churchland and Sejnowski, 1992). The ability of hidden neurons to extract higher-order statistics is particularly valuable when the size of the input layer is large. G. Baldassarre, S. Nolfi gives that Artificial Neural Networks are formalism widely use to encode robots controllers in evolutionary robotics research. Feed-forward neural controller in which the state of the motors is a function of only the current state of the sensors. 3) Recurrent Networks The third class is a recurrent network distinguishes itself from a feedforward neural network in that it has at least one feedback loop. A recurrent network may consist of a single layer of neurons with each neuron feeding its output signal back to the inputs of all the other neurons. There are no self-feedback loops in the network; it refers to a situation where the output of a neuron is fed back into its own input. 397
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME IV. OVERVIEW OF A ROBOT MOVEMENT EVOLUTION The study of robotics deals with the desire to synthesize some aspects of human function by the use of mechanisms, sensors, actuators, and computers . Robots are a rich and exciting field of Artificial Neural Networks . The robot will contain controller, here the controller might be thought of as the brain of the robot. It will decide the movement of the robot. The term controller is used to describe the computational portion of a mobile agent system. The controller receives the information from the robot’s sensors, Process this information and produces the actuator or motor commands that cause the robot to move or interact with its environment. In the broader field of autonomous robotics, control learning may focus on selected portions of a robot’s control abilities, such as object recognition , path planning  and localization [15,2], or error and fault accommodation. Sensors are also play major role in the movement of the mobile agents, here a sensor takes the information from the users and then transmits that information to controller then that controller will process that information. The Diane J.Cook, Juan C. Augusto, Vikamaditya R. Jakkula are given that sensors have been designed for position measurement, for detection of chemicals and humidity sensing , and to determine readings for light, radiation, temperature, sound, strain, pressure, position, velocity, and direction, and physiological sensing to support health monitoring.. Tracking and identifying people in an environment is an important issue in movement of the mobile agent systems. If the location of the person is identified, the system can serve the individual better by anticipating needs based on their preferences and delivering services based on when they commonly required. The technology which is often used to track individuals is motion sensors. Motion sensors have been used as a backbone of security systems for decades. However, while they can detect movement they cannot provide information to distinguish who (or what) produce the movement. There are different types of sensors are there in that main range sensors are ultrasonic or sonar, and radial laser sensors. Although laser range sensors offer a greater angular resolution, sonarsensor has reduced cost, is present on almost every robot platform, and requires the computation of a smaller raw data volume . According to A. Elfes (1989) occupancy grids can be used to store sensor data. There are two different sensing modes are there. They are first one is wireless sensors and second one is wired sensors. TABLE: 1. Different sensing modes and their applications Sensing type Strain and pressure Position, direction, distance and motion Light, radiation and temperature efficiency Solids, liquids and gases sprinkler, efficiency iButton Sound Common uses Floors, doors, beds, sofas, scales Security, locator, tracking, falls detection Security, location, tracking, health safety, energy Security and health, monitoring, pool maintenance, Used to identify people and objects Security, identification, context understanding TABLE: 2. Contrasting characteristics of wired and wireless sensors Wired sensor Cheaper sensors Pay for wiring Robust Need power source Wireless sensors More expensive No wiring Not as robust Batteries 398
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME A. Control System The control system  plays a major role in the movement of robots, control is the part of the system it processes the information and takes the decisions. Control is able to produce a decision/action sequence to reach desired goal given some information about the environment and the system itself. As indicated above, the statement of the goal implies the definition of a task and the control system must establish the strategy to accomplish this task. Here the control is defined as the ideas if intelligence and optimization. Intelligence is defined as the ability to plan and execute action sequences. A.L. Nelson, G.J. Barlow, L. Doitsidis, states that the control process becomes fully or partially an optimization process when the stated goal can be formulated as the global extreme(maximum or minimum) of an objective function. B. Path Planning The mobile agents will have to plan the path, According to Cecilia Garcia Cena, Pedro F. Cardenas, Roque Saltaren Pazmino , Lisandro Puglisi, Rafael Aracil Santonja(2013) the main goal on the cooperative tasks is that all the robotics agents could reach their target by avoiding obstacles without colliding between them and acting simultaneously. Where the path planning for each robotic agent is treated individually. The path planning procedure is composed by six main steps, as shown in Fig. 3. Fig. 3 Schematic diagram of the uncouple path planning strategy 399
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME An agent is defined as a software or hardware entity with specific skills and capabilities in order to cooperate and complete a common task. The IP-Camera agent is the hardware of the mobile agent. This sensor allows taking pictures of the robotic scene in which robotic agents perform the task in spite of the obstacles. The software agents are developed under C++ language and are listed below and: • Image processing agent: functions developed in order to process the pictures taken by the IPCamera and obtain information about the position of robots and obstacles in the scene. • Path planner agent: specific functions that generate and plan the robot’s trajectories. • Communication agent: this provides communication (TCP/IP and Bluetooth) among agents and it manages the information packets which are generated by the each agent. • Decision maker: this agent called engine or controller (Brain of the robot). It has the knowledge of the system and the goals. Therefore it takes decisions about the next step in the system. V. CONCLUSION AND FUTURE WORK In this paper I have proposed an intelligent mobile agent (robot). The intelligence of the robot is considered based on the use of a computational artificial neural networks used for the movement of the mobile agent. The robot uses an artificial neural network for solving TSP problem which occurred in the movement of the mobile agent. Solution for the TSP problem is that extracting the closely optimal path between the starting and ending point. Robotics is very useful in our real life, robots will work very effectively (or more efficiently) replace direct human presence in routine tasks, such as in the fields of agriculture and mining. “Robots which are capable of travelling where people cannot go” . Future research direction is the application of the self organizing map (SOM) in the movement of the mobile agent and localization. ACKNOWLEDGEMENTS The author would like to thank his brother Mr. K. Vinod Reddy Associate Prof. in Bharathiya Mahavidyalay, Amravati. For the encouragement to do the PhD. In Artificial Intelligence (implementation in Robotics) which is author’s interested area, and support in preparing this manuscript. REFERENCES 1. 2. Alexander Serenkoa,∗, Michael Dohanb, “Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence”, a Faculty of Business Administration, Lakehead University, 955 Oliver Road, Thunder Bay, Ontario P7B 5E1, Canada, b DeGroote School of Business, McMaster University, 1280 Main St. West, Hamilton, Ontario L8S 4M4, Canada. PP. 629-648. Andrew L. Nelson a,*, Gregory J. Barlow b, Lefteris Doitsidis c, “Fitness functions in evolutionary robotics: A survey and analysis”, a Androtics, LLC, PO Box 44065, Tucson, AZ 85733-4065, USA, b The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA, c Intelligent Systems & Robotics Laboratory, Department of Production Engineering & Management, Technical University of Crete, 73132, Hania, Greece. PP 345-370. 400
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. Antonio Sgorbissa *, Renato Zaccaria, “Planning and obstacle avoidance in mobile robotics”, DIST - University of Genova, Via Opera Pia 13, 16145 Genova, Italy, Robotics and Autonomous Systems 60 (2012) . PP. 628–638. Cecilia Garcia Cena a,c,*, Pedro F. Cardenas b,1, Roque Saltaren Pazmino a, Lisandro Puglisi a,2,Rafael Aracil Santonja a, “A cooperative multi-agent robotics system: Design and modeling”, a Centro de Automática y Robótica, Universidad Politécnica de Madrid, Jose Gutierrez de Abascal 2, 28006 Madrid, Spain b Universidad Nacional de Colombia, Colombia c Escuela Universitaria de Ingeniería Téncica Industrial. Ronda de Valencia, 3, 28012 Madrid, Spain. Christopher Stanton *, Mary-Anne Williams, “Robotics: State of the Art and Future Challenges, Imperial college Press, 2008, Innovation and Enterprise Research Laboratory, University of Technology, Sydney, Australia. PP. 1967-1972. Colin R. Davies, “An evolutionary step in intellectual property rights-Artificial intelligence and intellectual property”, University of Glamorgan, UK. PP. 601-619. Diane J. Cooka, Juan C. Augusto b,_, Vikramaditya R. Jakkula a, “Review Ambient intelligence: Technologies, applications, and opportunities”, a School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA b School of Computing and Mathematics, University of Ulster, UK, Pervasive and Mobile Computing 5 (2009) . PP. 277-298. R.J. Duro a,*, M. Graña b, J. de Lope c, “On the potential contributions of hybrid intelligent approaches to Multicomponent Robotic System development”, a Grupo Integrado de Ingeniería, Universidade da Coruña, Spain, b Grupo de Inteligencia Computacional, Universidad del País Vasco, Spain, c Grupo de Percepción Computacional y Robótica, Universidad Politécnica de Madrid, Spain, PP. 2635-2648. Edmund M.A. Ronald a,b,*, Moshe Sipper b, “Surprise versus unsurprise: Implications of emergence in robotics”, a Centre de Mathématiques Appliquées, Ecole Polytechnique, 91128 Palaiseau Cedex, France, b Logic Systems Laboratory, Swiss Federal Institute of Technology, CH-1015 Lausanne, Switzerland, Robotics and Autonomous Systems 37 (2001). PP. 19–24. Gianluca Baldassarre *, Stefano Nolfi, “Strengths and synergies of evolved and designed controllers: A study within collective robotics”, Laboratory of Autonomous Robotics and Artificial Life, Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche (LARAL-ISTC-CNR), Via San Martino della Battaglia 44, 00185 Roma, Italy. PP. 857-875. Honghai Liu a,*, George M. Coghill b, David J. Brown a, “Qualitative kinematics of planar robots: Intelligent connection”, a Institute of Industrial Research, University of Portsmouth, Portsmouth PO1 3QL, England, UK b Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK, Received 6 March 2006; received in revised form 22 January 2007; accepted 1 February 2007 Available online 12 February 2007. PP. 525-541. Jacek M. Zurada, “Introduction to Artificial Neural Systems”, Prof. of Electrical Engineering and of Computer Science and Engineering. Pg. No. 01-89. Jani J.T. Lahnaj(arvi∗ , Mikko I. Lehtokangas, Jukka P.P. Saarinen, “Estimating movements of a robotic manipulator by hybrid constructive neural networks”, Institute of Digital and Computer Systems, Tampere University of Technology, P.O. Box 553, Tampere FIN-33101, Finland. Neurocomputing 56 (2004). PP. 345 – 363. Ras¸it Ko¨kera,a *, Cemil O¨ za, Tarık C¸ akarb, Hu¨seyin Ekizc, “A study of neural network based inverse kinematics solution for a three-joint robot”, a Department of Computer Engineering, Sakarya University, 54187 Sakarya, Turkey, b Department of Industrial Engineering, Sakarya University, 54187 Sakarya, Turkey, c Department of Electronics 401
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME 15. 16. 17. 18. 19. 20. 21. Education, Sakarya University, 54187 Sakarya, Turkey, Robotics and Autonomous Systems 49 (2004) . PP. 227–234. Sándor T. Brassaia,b,*, Barna Iantovicsb, Cali Enachescua,b, “Artificial Intelligence in the path planning optimization of mobile agent navigation”, aPetru Maior University of Tirgu Mures, str. Nicolae Iorga, nr.1, Tirgu Mures 540088, Romania, bSapientia Hungarian University of Transilvania, O.p 9, C.p.4, Tirgu Mures, 540485 , Romania. Procedia Economics and Finance 3 ( 2012 ) 243 – 250. Sergio Guadarrama a,*, Antonio Ruiz-Mayor b, “Approximate robotic mapping from sonar data by modeling perceptions with antonyms”, a Fundamentals of Soft Computing Unit, European Centre for Soft Computing, Mieres, Asturias, Spain, b Departamento de Tecnología Fotónica, Universidad Politécnica de Madrid, Campus de Montegancedo, Boadilla del Monte, Spain. Information Sciences 180 (2010). PP. 4164–4188. Simon Haykin, “Neural Networks”, McMaster University Hamilton, Ontario, canada. Pg.no. 24. Sreekanth reddy kallem, ”Artificial Intelligence Algorithms”, Department of computer science, AMR Institute of Technology, Adilabad,J NTU, Hyderabad, A.P, India, ISSN: 22780661, ISBN: 2278-8727 Volume 6, Issue 3 (Sep-Oct. 2012), PP 01-08. Kulbhushan Verma, Manpreet Kaur and Palvee, “Comparative Analysis of Various Types of Genetic Algorithms to Resolve TSP”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 5, 2013, pp. 111 - 116, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. Kabeer Mohammed and Dr.Bhaskara Reddy, “Optimized Solution for Image Processing Through Mobile Robots Working as a Team with Designated Team Members and Team Leader”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 3, 2013, pp. 140 - 148, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. Gulwatanpreet Singh, Surbhi Gupta and Baldeep Singh, “ACO Based Solution for TSP Model for Evaluation of Software Test Suite”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 75 - 82, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. AUTHOR’S INFORMATION SREEKANTH REDDY KALLEM, Received his BSc degree from Kakatiya University, Warangal in 2006. In 2009, He was awarded his MCA degree in computers from JNT University. Since 2009, He has been working as Asst. Prof. in AMR Institute of Technology. His research interests lie in the areas of implementation of Artificial Intelligence and Neural Networks in robotics, Mobile Computing and Networking. 402