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CONTROL OF MOBILE ROBOT USING C++
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  This article can be downloaded from http://www.ijmerr.com/currentissue.php 429 Int. J. Mech. Eng. & Rob. Res. 2014D Elayaraja et al., 2014ISSN 2278 – 0149 www.ijmerr.comVol. 3, No. 2, April, 2014© 2014 IJMERR. All Rights Reserved Research Paper INTELLIGENT CONTROL OF MOBILE ROBOT USING C++ D Elayaraja 1 *, R Ramakrishnan 2 , M Udhayakumar 3  and S Ramabalan 4 *Corresponding Author: D Elayaraja,      cape.duraisamy@gmail.com A robot is an electro-mechanical device that can perform autonomous or pre-programmed task.A robot can work or carry out an assigned task successfully with the help of a program. Theobjective of this work is to make the navigation of the robot more precise and add intelligence toit by using programming software. In this work C++ compiler is used to stimulate the mobilerobots within these environments. Controller programs helped to read input values and showthe required simulation in a graphic window. The goal reaching character of the mobile robot issuccessfully carried out using C++ program. Keywords: Mobile robot, Intelligent Control, Goal reaching, C++ Program INTRODUCTION Autonomous mobile robots need to bringeverything along with them, including a powersupply and a brain. The power supply istypically an array of batteries, which adds alot of weight to the robot. The brain is alsoconstrained because it has to fit into the robot,not weigh a ton, and be frugal about suckingpower out of the batteries.Parhi et al used Rule base technique andpetri net modelling to avoid collision amongrobots one model of collision free pathplanning and also modelled fuzzy-logictechnique and both the models arecompared. They found that the rule-basedtechnique has a set of rules obtained throughrule induction and subsequently with 1 Associate Professor, K.Ramakrishnan College of Engineering, Samayapuram, Trichy, Tamilnadu, India. 2 PG student, K.Ramakrishnan College of Engineering, Samayapuram, Trichy, Tamilnadu, India. 3 Asst.Professor, K.Ramakrishnan College of Engineering, Samayapuram, Trichy, Tamilnadu, India. 4 Principal, E.S.Pillay Engineering College,Nagapattinam, Tamilnadu, India. manually derived heuristics. This techniqueemploys rules and takes into account thedistances of the obstacles around the robotsand the bearing of the targets in order tocompute the change required in steeringangle (Dayal Ramakrushna Parhi et al  .,).Fainekos et al. provided a tractablesolution to the motion planning problem fordynamics models of mobile robots andtemporal logic motion planning problem formobile robots are modelled by second orderdynamics (Georgios E et al  .,).Raquel Ros et al interested in the actionselection and coordination for joint multi-robottasks, motivated by a prototype environmentof robot soccer. They have successfullyapplied Case-Based Reasoning (CBR)  This article can be downloaded from http://www.ijmerr.com/currentissue.php 430 Int. J. Mech. Eng. & Rob. Res. 2014D Elayaraja et al., 2014 techniques to model the action selection of ateam of robots within the robot soccerdomain. However, their previous approachdid not address the dynamic intentionalaspect of the environment, in particular, inrobot soccer, the presence of adversaries.Many efforts aim at modelling the opponentsin particular when the perception iscentralized. Instead, they addressed a robotsoccer framework in which the robots are fullydistributed, without global perception orglobal control (Raquel Ros et al  .,).Yun-Hui Liu et al proposed a tangent graphfor path planning of mobile robots amongobstacles with a general boundary. Thetangent graph is defined on the basis of thelocally shortest path. It has the same datastructure as the visibility graph, but its nodesrepresent common tangent points on ob-stacle boundaries, and its edges correspondto collision-free common tangents betweenthe boundaries and convex boundary seg-ments between the tangent points. Thetangent graph includes all locally shortestpaths and is capable of coping with pathplanning not only among polygonal obstaclesbut also among curved obstacles (Liu YH,Arimoto S,1992).Kamal Kant et al present a novel approachto solving the trajectory planning problem(TPP) in time-varying environments. Theessence of their approach lies in a heuristicbut natural decomposition of TPP into twosub-problems: (i) planning a path to avoidcollision with static obstacles and (ii) planningthe velocity along the path to avoid collisionwith moving obstacles (Kant K, Zucker SW,1986).Fujimura K et al. performed a study onproblems in planning a collision-free path fora robot in a time varying environment. It isassumed that an obstacle moves along aknown path. The formulation also allows thedestination point (the point to be reached) tomove along a pre-determined path. A newconcept of ‘accessibility’ from a point to amoving object is introduced and is used todetermine a collision free path. An en-vironment which contains some uncertaintyin which the robot needs to see and possiblyplan an alternative path is also considered.The ability to deal with moving obstacles isuseful in a variety of visual tasks such as thenavigation of an autonomous vehicle and theinteraction between robot arms (Fujimura K,Samet H, 1988).Lamadrid JG et al. addressed the problemof planning motions for a mobile robot in thepresence of objects moving on unknowntrajectories and with unknown velocities. Therobot must follow a predefined path with agiven tolerance and reach its destination bya given time without colliding with anyobstacle. Objects are represented bynonintersecting discs in the plane, and therobot by a point. The proposed method relieson the use of sensors to detect obstacles,and interleaves path planning with execution.Experimental results obtained both insimulation and with a real robot are shownby them (Lamadrid JG, Gini ML, 1990).Slack MG et al. presented an integratedroute planning and spatial representationsystem that allows paths to be calculated indynamic domains. The path planner finds the“best route” through a given n-dimensionalspace.The algorithm takes into account thefollowing: Capabilities of the robot executinggenerated plans, traversability of space, andinteractions with both predictable and un-predictable dynamic objects. Planning robotmovement in dynamic environments de-mands that the dynamic aspects of theenvironment be modelled in at least as muchdetail as the movements of the robot. Theyhave created a representation system that  This article can be downloaded from http://www.ijmerr.com/currentissue.php 431 Int. J. Mech. Eng. & Rob. Res. 2014D Elayaraja et al., 2014 allows dynamic aspects of the environmentand performance aspects of the robot to beeasily modelled. It also integrates this modelwith a route-planning algorithm. This systemhas been extended into an incremental routeplanner which can be used for real-timetactical planning in unpredictable domains(Slack MG, Miller DP, 1987).Fiorini P et al. presented a simple and effi-cient approach to the computation ofavoidance manoeuvres among movingobstacles. The method is discussed for thecase of a single manoeuvring object avoidingseveral obstacles moving on known lineartrajectories. The srcinal dynamic problem istransformed into several static problemsusing the relative velocity between themanoeuvring object and each obstacle(Fiorini P, Shiller Z, 1993).A paper by LaValle SM et al. presents thefirst randomized approach to kinodynamicplanning (trajectory planning/design). Thetask is to determine control inputs to drive arobot from an initial configuration and velocityto a goal configuration and velocity whileobeying physically based dynamical modelsand avoiding obstacles in the robot'senvironment. They considered genericsystems that express the nonlinear dynamicsof a robot in terms of robot's high dimensionalconfiguration space. Kinodynamic planningis treated as motion planning problem in ahigher- dimensional state space that has bothfirst order, differential constrains andobstacle-based, global constraints (LaValleSM, Kuffner JJ, 200).Ahuactzin et al. has formulated a geneticalgorithm for motion planning of robots whichshows that the path planning problem canbe expressed as an optimization problem andthus solved with a genetic algorithm. Weillustrate this approach by building a pathplanner for a planar arm with two degree offreedom, and then we demonstrate the vali-dity of the method by planning paths for aholonomic mobile robot (Ahuactzin, El-Ghazali Talbi et al  .,). KINEMATICS OF WHEELEDMOBILE ROBOT A unicycle mobile robot is an autonomous,wheeled vehicle capable of performingmissions in fixed or uncertain environments.The robot body is symmetrical around theperpendicular axis and the centre of mass isat the geometrical centre of the body. It hastwo driving wheels that are fixed to the axisthat passes through and one passive wheelprevents the robot from tipping over as itmoves on a plane. Figure 1: A Unicycle Mobile Robot The nonholonomic kinematic constraints aredescribed byA(q)q = 0...(1)And the following relation can be obtainedA(q)S(q) = 0...(2)Whereis composed of linearly independent vectorsin the null space of a (q) from (1) and (2), (n-m)-dimensional vector exists such thatq = S(q)z...(3) ã

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