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  1. 1. Mr. Anil Kumar Gona / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.157-162 Development and Investigation on an Embedded System based Control for Networking Mobile Robots Mr. Anil Kumar Gona ,M.Tech Asst .Professor Dept.of E.C.E, L.N.B.C.I.E.T, SataraAbstract In today’s hi-tech and hi -precision world, engineering fields; electrical, mechanical androbot finds its application in many areas to carryout computing. While mechanical involvesoperations that are either routine, highly complex mechanisms and multi-body dynamics, electricaland critical, hazardous or of high-precision nature. pertains to sensing and controlling, computingRobots when networked offer many benefits such as interfaces the above two fields in the form ofincreased maneuverability and efficiency. This hardware and software, also called embeddedpaper makes a survey on the present developmental systems.status and details the design features of networkingmobile robots using embedded system based control. While conceiving a single robot forIt is intended to use this work as basis for future carrying out a specific operation, such as a robotresearch work in the area of cooperative behavior of arm could be less complex, yet it has to have inmobile robots. itself the computing capability. An embedded system bridges this gap which is a special-purposeIndex Terms – Mobile Robot, Embedded System, computer system designed to perform one or moreControl, Network, Microcontroller, Vision system dedicated functions, often with real-time computing environment. It is usually embedded asI. INTRODUCTION part of a complete device including hardware, Practical application is an important software and mechanical parts. The advantage ofcomponent of any scientific and engineering research an embedded system is that it can be programmedand more so in the field of robotics. The applications of according to the application in hand, „applicationrobot in day-to-day activities for industrial and time specific‟ providing reconfiguration of the samecritical fail-safe operations are increasing. Some of the robot for different operations.broader examples are agriculture, automotive The main objective of this paper is tomanufacturing, construction, entertainment, health-care, provide an overview of an embedded systemlaboratories, security and surveillance, military, mining, based control for networking mobile robots inwarehouse operations etc. Robot for industrial order to understand and carryout the following:applications include critical and complex assemblyoperations which are of repetitive nature, performing • Design and development of an embeddedroutine operations such as pick-and-place, process system for robot experiments and trainingcontrol operations in nuclear and robotic arm in space scenarios related to the operation, programming,to perform time-critical and fail-safe operations with and control.minimum human intervention. Applications such aswalking robot for human health care of elderly or • Investigation and focus on the basic principleshandicapped people and for recreational activities are of networked intelligent mobile robots and furtheralso on the increase. emphasis on features such as robotic arm Robot is a device employing sensors, manipulators, grippers or the vision systemelectronics, computing and control features to carry out capability.certain intended functions and falls in the realm ofmechatronics. Cognition and artificial intelligence • Perform theoretical and experimentaltechniques are implemented in robot to make it think evaluation on the networking methodologies andand act in the way humans do. control logic, in order to develop a comprehensive The use of multiple robots enables networking, understanding between the robots.resulting in various industrial applications viz., materialhandling, material transfer, machine loading and II. LITERATURE SURVEYunloading, spot welding, spray coating, assembly The basic principle and design of variousoperations and inspection. They also find applications components of robot such as manipulators, endin micro- surgery areas in performing time-critical and effectors are dealt with in detail in textbookslife-saving operations. [1][2][3]. Intelligent autonomous robots find itsThe science of robot encompasses three main application in many areas as explained in Sec. I. 157 | P a g e
  2. 2. Mr. Anil Kumar Gona / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.157-162Justin [4] discusses the mobile robot/sensor networks capture and escape.that have emerged as tools for environmentalmonitoring, search and rescue, exploration and Obstacles, motion control, logic and estimationmapping, evaluation of civil infrastructure and military are dealt in detail by the following authors. Yangoperations. Shibata et al. [5] describes the realization of Tian-Tian et al. [15] considered the problem ofa modular distributed control architecture specifically formation control and obstacle avoidance for adesigned to control locally intelligent robot agents. On group of nonholonomic mobile robots. On thethe vision algorithms, Jose & Giulio [6] discusses the basis of suboptimal model predictive control, twoimplementation of a set of visually based behaviors for control algorithms were proposed. Liu Shi-Cai etnavigation. More specifically, Thierry et al. [7] has a7. [16] proposed the formation control andpresented the idea of using 3D visual cues when obstacle avoidance problems that dealt with atracking a visual target, in order to recover some of the unified control algorithm, which allowed the3D characteristics of robots such as depth, size and follower to avoid obstacle while maintainingkinematic information. The basic requirements for such desired relative bearing or relative distance froma 3D vision module to be embedded on a robotic head the leader. The leader-follower robot formationare discussed. was modeled and controlled in terms of theA number of papers discuss on the communication relative motion states between the leader andnetwork and control techniques. R.M Kuppan Chetty et follower robots. Ralph P. Sobek et al. [17]al. [8] discusses a layered reconfigurable distributed presents a distributed hierarchical planning andplanning and control strategy for multiple mobile robots execution monitoring system and itsin a leader-follower formation framework that implementation on an actual mobile robot. Thecombines formation planning, navigation and active planning system was a distributed hierarchicalobstacle avoidance.Firmansyah [9] has developed a domain independent system called Flexibleprototype of modular networked robot for autonomous Planning System (FPS). It is a rule based planmonitoring with full control over web through wireless generation system with planning specific andconnection. The robot is equipped with a particular set domain specific rules. Luca Consolini et al. [18]of built-in analyzing tools and appropriate sensors to dealt with leader–follower formations ofenable independent and real-time data acquisition and nonholonomic mobile robots, introducing aprocessing. The paper focusses on the microcontroller- formation control strategy alternative to thosebased system to realize the modularity. Ricardo Carelli existing in the literature.et al. [10] proposed an autonomous tracking control Pertaining to robot intelligence, Volker Turau [19]system and a control structure to combine autonomous presented a model for a robot control system thatand teleoperation commands in a bicycle-type mobile is under development for use in a manufacturingrobot. Teleoperation with visual access to the robot‟s cell, which consists of various assembly stationsworkspace is integrated via a joystick with the and a material storage system. KAMRO robot willautonomous operation of the robot. Behrokh be able to analyze failures and to recover in anKhoslmevis et al. [11] discuss the development ideas intelligent manner. S.G. Tzafestas et al. [20] studyand merits of a single centralized station to provide the problem of control and motion planning in thesupervision, sensing, control, intelligence and perhaps presence of uncertainty for a mobile robot subjectpower to a colony of robots, organized for the to state and actuator constraints, which travelsperformance of specific tasks. The consequence of such along a predetermined path inside a terrain withan approach is that each robot can be less complex, moving obstacles. proposed an optimal controlsmaller in size and economical, as no on-board sensing, law which was derived for path tracking control ofcontrol and intelligence are required. Ollero et al. [12] a class of Wheeled Mobile Robots (WMR) orpresented the Navigation and Operation System (NOS) Automated Guided Vehicles (AGV‟s) with frontfor a multipurpose industrial autonomous mobile robot steering wheel. describe a path tracking controlfor both indoor and outdoor environments. All system developed for autonomous mobile robotsprocesses in the NOS have been integrated in a driven by wheels.uses a robot vision system whichdistributed hierarchical architecture considering the finds a target object and focuses on it from areal-time constraints. Alberto Elfes [13] discusses a specified distance. A neural network is used as adistributed control system that provides scheduling and decision maker which determines how to movecoordination of multiple concurrent activities on a the robot to reach the target object, on the basis ofmobile robot. the image acquired through the camera. Dayal Ramakrushna Parhi et al. [24] address anOn the learning aspects of a robot, Nagata et al. [14] intelligent path planning of multiple mobiledescribe the reason and instinct networks; and pseudo robots. Gordon Wells et al. [25] provide aimpedance control, together with a training process in comparative survey of existing visual robotwhich they gave ten mobile robots with variety of positioning methods and present a new techniquehabits and had them play a cops-and-robbers game. based on neural learning and global imageThrough training, the robots learned habits such as descriptors which overcomes many of the 158 | P a g e
  3. 3. Mr. Anil Kumar Gona / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.157-162limitations. A.F.T. Winfield et al. [27] describe a signals back to them. Motor stepper drivercommunications and control infrastructure for (controller) magnifies the pulse width modulationdistributed mobile robotics, which makes use of from the processor to drive the motors. SensorsWireless Local Area Network (WLAN) and Internet viz., magnetic, gyros, accelerometer, tactile, forceProtocols (IPs). and vision sensor etc are commonly used to senseS.H Somashekhar et al. [27] present the precision the various signals and send them to the processor.machining used to drill microholes wherein various The core of control system is either a DSPmanufactuing processes are controlled manually which processor or microcontroller.when replaced by an embedded system based control,will improve the process quality and accuracy. B.Gokul B. Embedded System for a Networked Robotet al. [28] present some of the preliminary results ofembedded system based industrial fault diagnoser.III. EMBEDDED SYSTEM BASEDCONTROL FORNETWORKING MOBILEROBOTS - DESIGN ASPECTS The technical design aspects and methodsplanned to develop the proposed Embedded systembased Control for Networking mobile Robots (ECNR)is described in this section. Fig. 1 shows the blockrepresentation of a mobile robot.A. Mobile Robot Descriptionplanning and control strategies and suitable for usage indevelopment of an ECNR. The E-Puck mobile robot ispreferred because of its miniature size and availabilityof multiple sensors and actuators matching therequirements of ECNR. While the Amigobot isexpensive, it has the range capability for indoor as wellas outdoor operations and has a built-in wireless.D. Software Architecture of Embedded System Fig.2 Embedded system based controlTypical software system architecture of an ECNR is schematic for networked mobile robotshown in Fig. 3 below. Embedded system configuration for a networked mobile robot is shown in Fig. 2 below. It is configured around a server with a backup in order to provide the required fail-safe functioning during critical operations. The server basically takes -off the load of the individual robots and all the main functions are deposited and controlled from the main server while the backup functions as redundant or hot-standby. The configuration and various elements are described in the para above. C. Choice of Mobile Robots Some of the known mobile robots, viz., E-Puck and Amigobots provide necessary knowledge on motionFig.1 Block representation of a mobile robot Embedded Controller is the brain of themobile robot and is responsible for all actions.According to the function planning, the systemhardware constitutes the following basic elements suchas Sensors, Processor, Controller, Actuators,Communication (user interface) and Power supply.Processor unit is responsible for processing the signalreceived from other modules and to send proper error 159 | P a g e
  4. 4. Mr. Anil Kumar Gona / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.157-162 operations, an evenly distributed line sampling detection needs to be implemented. The result is expected to give definite information about the block‟s position and its rotation angle in the horizontal direction. HSI model uses H heft as the main partition parameter. The basic image processing algorithm to obtain HSI parameters from RGB input are shown below. 1( R + G + I= B) (1) 3 3 [min( R, G, S=1− B)] (2) R+G+B [( R − G ) + ( R − B)] / 2 H = arccos{ } (3)Fig.3 Software system architecture [( R − G ) 2 + ( R − B )( G − B)]1 / 2 The drive layer interfaces the hardware and The processing algorithm should be simpletask layer. The hardware layer consists of various enough to occupy less memory and fast enoughsensors for detecting the environment, path constraints, but at the same time, capture the image featuresmotion, speed etc; gyro sensor provides the orientation within the real time period.and the CMOS image sensor, the vision capability thatis basic to mobile robots. The sensor information is processed by the IV. ROBOT KINEMATICS Robots movement involves bothvarious modules of the task layer, viz., in motion kinematics as well as dynamics. While details onplanning, rule based decisions and generating the these are considered out of scope of this paper, acorrection/commands for communication to the motor small brief is provided here.control. Robot movement consists of translation as well as rotation and these have to be calculated withE. Vision Unit reference to a fixed reference coordinate system in Vision being the complex of all the sensors, a order to estimate the movement of every singlebrief description on the typical features is discussed in robot. In a robot, each link and parent joint formthis section. This unit consists of a CMOS image their own local coordinate system. Rotation,sensor, dual-port RAM and necessary real-time features translation and other transforms can be performedfor communication with DSP. The sensor will have a through matrix multiplication as depicted below.normal sampling rate of 30-50 frames per second. This Translation (D):sensor has features for implementing the whiteequilibrium, exposure control, saturation and tonality 1 0 0 TXcontrol. The image captured and stored in the RAM isused by the DSP during every sampling period. The 0 1 0 TYprocessing involves reading of a static image at the (4)target position and processing. Thus the system can 0 0 1 TZmake a judgment through the sampled data of severallines. Since the memory requirement for image storage 0is not very large, a high speed dual-port RAM is 0 0 1adapted. Rotation about x (Rx), y (Ry), and z (Rz) axes:F. Visual Algorithm 1 0 0 0 cosθ 0 sinθ 0 cosθ −sinθ 0 0 CMOS Image sensor provides RGB data. The 0hefts of RGB own a strong relation amongst each other cosθ −sinθ 0 0 1 0 0 sinθ cosθ 0 0and make it difficult to process. HSI model is used for 0 sinθ cosθ 0 −sinθ 0 cosθ 0 0 0 1 0 (5)the object identification algorithm. It separates theintensity data from the colour information. Consideringthe system storage capacity associated with such 0 0 0 1 0 0 0 1 0 0 0 1 160 | P a g e
  5. 5. Mr. Anil Kumar Gona / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.157-162 A. Locomotion of Wheeled Robots VI. EXPERIMENTAL VERIFICATION The locomotion of wheeled robots may be carried out The following sets of broad level experiments in any of the following four standard methods: need to be planned to evaluate the total system performance. • Differential Drive, eg – Pioneer 2-DX • Direct and inverse kinematics problems • Car Drive (Ackermann Steering) • Study of different controllers and the effects of • Synchronous drive, eg – B21 control parameters on individual mobile robots • Mecanum wheels, eg – XR 4000 • Robot motion planning in different control modes, in different coordinate systems etc B. Position Estimation The position estimation can be carried out by the „Dead • Experiments on path planning, trajectory Reckoning‟ method as explained in Fig. 4 below. tracking for each of the individual mobile robots • Experiments on obstacle avoidance, localization, goal seeking and other features that are applicable • Experiments on Mobile robot applications such as search & surveillance, navigation and Multi robot coordination • Experiments on networking with the VisionFig. 4 Position systemestimation using dead reckoning method VII. CONCLUSION ∆r − ∆l ECNR when implemented can∆θ = (6) provide an easier access to the available d hardware and software facilities that help to ∆r + ∆l practice realistic robot networking using the∆s = (7) functionalities and programming capabilities of the real robots for developing various2 industrial applications. Thus, an attempt is made in this paper to provide an over view for V. Ecnr Development Methodology a researcher interested this field with the The ECNR development can be planned in status, and a broad survey of available phases as described below. Initially system modelling lieterature and an introduction on the basic could be done, followed by control law design. module functionalities. Verification by simulation is carried out at each and every stage. Next, the embedded control system is implemented and finally the complete system is REFERENCES [1] John J Craig, “Introduction to Robotics – realized after validation and testing by experiments. Mechanics and Control”, Third Edition, Pearson Education, 2009 A. Phase – I [2] Robert J Schilling, “Fundamentals of • Conceptual design and modeling of embedded Robotics – Analysis and Control”, systems and robotics experiments Prentice Hall of India Ltd., 2007 [3] Chris Morgan, “Robots – Planning and • Graphical user interface design for the experiments Implementation”, IFS Publications [4] C.Justin, Rafael Fierro, “Mobile • Integration of mobile robots with the embedded robotic sensors for perimeter detection control and vision systems and tracking”, ISA Transactions, Vol. 46, Issue 1, pp. 3-13, 2007 B. Phase – II [5] Junichi Shibata, Genichi Yasuda, & • Realization of the Embedded Control for Networking Hiroyuki Takai. “Implementation of a Robots and integration of the systems modular control system for multiple intelligent mobile robots.” In • Experimental investigation and verification of user Proceedings of the 3rd Conference on interface systems through LAN JSME Robotics and Mechatronics 161 | P a g e
  6. 6. Mr. Anil Kumar Gona / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.157-162 Symposia, pp. 44-47, 1998.[6] S.V.Jose, S.Giulio, “Embedded visual behaviors for navigation”, Robotics and Autonomous Systems, Vol. 19, Issues 3-4, pp. 299-313, 1997[7] V.Thierry, Emmanuelle Clergue, Reyes Enciso, Herve Mathieu, “Experimenting with 3D vision on a robotic head”, Robotics and Autonomous Systems, Vol. 14, Issue 1, pp 1- 27, 1995[8] Kuppan Chetty RM, Singaperumal M, Nagarajan T and Tetsunari Inamura, “Coordination Control of Wheeled Mobile Robots – A Hybrid Approach”, Int. J. Modelling, Identification and Control, Paper No. PCR03_IJMIC, (In press)[9] I.Firmansyah, B. Hermanto and L.T. Handoko, “Control and Monitoring System for Modular Wireless Robot”, Proc. of Industrial Electronics Seminar, Indonesia, 2007[10] Ricardo Carelli, Guillermo Forte, Luis Canali , Vicente Mut, Gaston Araguas, Eduardo Destefanis, “Autonomous and teleoperation control of a mobile robot”, Mechatronics, Vol.18, pp.187–194, 2008[11] Behrokh Khoslmevis and George Bekey, “Centralized Sensing and Control of Multiple Mobile Robots”, Computers ind. Engng Vol. 35, Nos 3-4, pp 503-506, 1998[12] A.Ollero, A. Mandow, V. F. Munoz and J. Gomez De Gabriel, “Control Architecture for Mobile Robot operation and navigation”, Robotics & Computer-Integrated Manufacturing, Vol. 11, No. 4, pp. 259-269, 1994[13] Alberto Elfes, “A distributed control architecture for an autonomous mobile robot”, Artificial Intelligence, Vol. 1, No. 2, 1986[14] S. Nagata. T. Kimoto. and K. Asakawa. “Control of Mobile Robots with Neural Networks”, Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 1015, Kamikodanaka Nakahara-ku, Kawasaki 211, Japan 162 | P a g e