Robot manipulators trends and development 2010 Part 11 potx

RobotManipulators,TrendsandDevelopment392 avoided by introducing a safety margin ranging from 0.1-2.5 millimeter at the both ends of the semicircle geometry. The numbers of sampling measurement points depend on the method employed so the sampling points of every method do vary depend on the method employed. It is anticipated that the tracking error value will be quite high in certain slope region of contour gradient (Prabuwono et al., 2009). Fig. 12 shows the four degrees of freedom SCARA robot that used in this study. Fig. 12. The four degrees of freedom SCARA robot. 5.2 Results The actual contour traced and the tracking error along contour, matching the semicircle geometry of radius 40 millimeter is plotted. For adapting gradient method, the enlargement of mean of tracking error with the value of - 0.3773 millimeter and the standard deviation of tracking error with the value of 2.3085 millimeter are shown in Fig. 13 and Fig. 14 respectively. The safety margin of 0.1 to 1 millimeter is allowed at the beginning and near to the end of semicircle object in order to avoid measuring the very high slope at those regions. The adapting gradient measuring advance parameter of 1 millimeter is chosen for this contour following experiment. The total sample of good 79 points was collected over 80 millimeter horizontal measuring distance. Fig. 13. Contour traced along half circle geometry with adapting gradient method. Fig. 14. Tracking error along half circle geometry with adapting gradient method. For staircase method, the enlargement of mean of tracking error with the value of 3.4011 millimeter and the standard deviation of tracking error with the value of 1.8412 millimeter are shown in Fig. 15 and Fig. 16 respectively. The safety margin of 0.1 to 1 millimeter is allowed at the beginning and near to the end of semicircle object in order to avoid measuring the very high slope at those regions. The staircase measuring advance parameter of 1 millimeter is chosen for this contour tracking experiment.The total good sample of 78 points was collected over 80 millimeter horizontal measuring distance. Fig. 15. Contour traced along half circle geometry with staircase method. PerformanceEvaluationofAutonomousContourFollowingAlgorithmsforIndustrialRobot 393 avoided by introducing a safety margin ranging from 0.1-2.5 millimeter at the both ends of the semicircle geometry. The numbers of sampling measurement points depend on the method employed so the sampling points of every method do vary depend on the method employed. It is anticipated that the tracking error value will be quite high in certain slope region of contour gradient (Prabuwono et al., 2009). Fig. 12 shows the four degrees of freedom SCARA robot that used in this study. Fig. 12. The four degrees of freedom SCARA robot. 5.2 Results The actual contour traced and the tracking error along contour, matching the semicircle geometry of radius 40 millimeter is plotted. For adapting gradient method, the enlargement of mean of tracking error with the value of - 0.3773 millimeter and the standard deviation of tracking error with the value of 2.3085 millimeter are shown in Fig. 13 and Fig. 14 respectively. The safety margin of 0.1 to 1 millimeter is allowed at the beginning and near to the end of semicircle object in order to avoid measuring the very high slope at those regions. The adapting gradient measuring advance parameter of 1 millimeter is chosen for this contour following experiment. The total sample of good 79 points was collected over 80 millimeter horizontal measuring distance. Fig. 13. Contour traced along half circle geometry with adapting gradient method. Fig. 14. Tracking error along half circle geometry with adapting gradient method. For staircase method, the enlargement of mean of tracking error with the value of 3.4011 millimeter and the standard deviation of tracking error with the value of 1.8412 millimeter are shown in Fig. 15 and Fig. 16 respectively. The safety margin of 0.1 to 1 millimeter is allowed at the beginning and near to the end of semicircle object in order to avoid measuring the very high slope at those regions. The staircase measuring advance parameter of 1 millimeter is chosen for this contour tracking experiment.The total good sample of 78 points was collected over 80 millimeter horizontal measuring distance. Fig. 15. Contour traced along half circle geometry with staircase method. RobotManipulators,TrendsandDevelopment394 Fig. 16. Tracking error along half circle geometry with staircase method. For sweeping radius method, the enlargement of mean of tracking error with the value of 0.2101 millimeter and the standard deviation of tracking error with the value of 3.2663 millimeter are shown in Fig. 17 and Fig. 18 respectively. The safety margin of 0.1 to 1 millimeter is allowed at the beginning and near to the end of the semicircle object in order to avoid measuring the very high slope at those regions. The sweeping radius parameter of 1 millimeter is chosen for this contour tracking experiment. The total sample of 67 points was collected over 80 millimeter horizontal measuring distance. Fig. 17. Contour traced along half circle geometry with sweeping radius method. Fig. 18. Tracking error along half circle geometry with sweeping radius metohd. 6. Performance Evaluation Fig. 19 summarizes all different methods for path traveling in order to evaluate their efficiency among all algorithms or methods implemented previously. The efficiency is measured with regard to the least tracking error standard deviation value and the shortest distance traveled. The best is assumed to be the least tracking error standard deviation value with the shortest sampling distance. In Fig. 19, the adapting gradient method follows path 1A to 2A, while the sweeping radius method starts from path 1B to 2B. The staircase method is the path that started from 1B to 4D. Fig. 19. Path comparison among three different contour following methods. PerformanceEvaluationofAutonomousContourFollowingAlgorithmsforIndustrialRobot 395 Fig. 16. Tracking error along half circle geometry with staircase method. For sweeping radius method, the enlargement of mean of tracking error with the value of 0.2101 millimeter and the standard deviation of tracking error with the value of 3.2663 millimeter are shown in Fig. 17 and Fig. 18 respectively. The safety margin of 0.1 to 1 millimeter is allowed at the beginning and near to the end of the semicircle object in order to avoid measuring the very high slope at those regions. The sweeping radius parameter of 1 millimeter is chosen for this contour tracking experiment. The total sample of 67 points was collected over 80 millimeter horizontal measuring distance. Fig. 17. Contour traced along half circle geometry with sweeping radius method. Fig. 18. Tracking error along half circle geometry with sweeping radius metohd. 6. Performance Evaluation Fig. 19 summarizes all different methods for path traveling in order to evaluate their efficiency among all algorithms or methods implemented previously. The efficiency is measured with regard to the least tracking error standard deviation value and the shortest distance traveled. The best is assumed to be the least tracking error standard deviation value with the shortest sampling distance. In Fig. 19, the adapting gradient method follows path 1A to 2A, while the sweeping radius method starts from path 1B to 2B. The staircase method is the path that started from 1B to 4D. Fig. 19. Path comparison among three different contour following methods. RobotManipulators,TrendsandDevelopment396 It is clearly seen in that the staircase method has the longest path followed by the adapting gradient method. The shortest distance is done by the sweeping radius method. With the same speed, it seems that the staircase method takes the longest time while sweeping radius is the fastest of all methods. All the results are tabulated in Table 1. The adapting gradient method consumes medium teaching time at standard deviation value of 2.3085 millimeter, while the staircase method consumes the longest teaching time at standard deviation value of 1.8412 millimeter. The sweeping radius method is very efficient in term of shortest teaching path but its standard deviation value of 3.2663 is a bit high. Criteria Adapting Gradient Staircase Sweeping Radius Mean of Error -0.3773 3.4011 0.2101 Standard Deviation 2.3085 1.8412 3.2663 Path Length Medium Long Shortest Table. 1. Summaries of the results for three different contour following methods. 7. Conclusion In this study, the performance evaluations of autonomous contour following task with three different algorithms have been performed for Adept SCARA robot. A prototype of smart tool integrated with sensor has been designed. It can be attached and reattached into robot gripper and interfaced through I/O pins of Adept robot controller for automated robot teaching operation. The algorithms developed were tested on a semicircle object of 40 millimeter radius. The semicircle object was selected because it exhibits the stringent test bed which provides the changing gradient gradually from steepest positive slope into zero slope of flat curve in the middle and finally to steepest negative slope. The adapting gradient method consumes medium teaching time at reasonable accuracy of standard deviation value of 2.3085 millimeter, while the staircase method consumes the longest teaching time at standard deviation value of 1.8412 millimeter. The sweeping radius method is very efficient in term of shortest teaching path but its standard deviation value of 3.2663 is a bit high. It can be concluded that the staircase method is the most accurate method, while the sweeping radius method has the shortest teaching path. These tests exhibit the performance of algorithms used which prove its possibility to be applied in the real world application. For the future, automatic curve radius determination between straight line segments can be improved by integrating vision system for the automation of top view (X-Y coordinate) edge finding and path planning. The integration of vision system with the present study will improve the automation level of the project from two to three dimensional capabilities. 8. References Adolfo, B.; Sadek, C.A.A. & Leszek, A.D. (2001). Predictive sensor guided robotics manipulators in automated welding cells. Journal of Materials Processing Technology, Vol. 109, No. 1-2, February 2001, 13-19, ISSN 0924-0136 Andersson, J.E. & Johansson, G. (2000). Robot control for wood carving operations. Mechatronics, Vol. 11, No. 4, June 2001, 475-490, ISSN 0957-4158 Awahara, M. & Taki, K. (1979). Tracking control for guiding electrodes along joints by pattern detection of welding groove. Transactions of the Society of Instrument and Control Engineers, Vol. 15, 492 Gopalakrishnan, B.; Tirunellayi, S. & Todkar, R. (2004). Design and development of an autonomous mobile smart vehicle: A mechatronics application. Mechatronics, Vol. 14, No. 5, 491-514, ISSN 0957-4158 Hanright, J. (1984). Selecting your first arc welding robot – a guide to equipment and features. Welding Journal, Vol. 1, 41-45 Hewit, J. (1996). Mechatronics design – the key to performance enhancement. Robotics and Autonomous Systems, 135–142, ISSN 0921-8890 Ikeuchi, K. & Suehiro, T. (1994). Towards an assembly plan from observation, Part I: Task recognition with polyhedral objects. IEEE Transactions on Robotics and Automation, Vol. 10, No. 3, 368-385, ISSN 1042-296X Inoue, K. (1979). Image processing for on-line detection of welding process (report 1): simple binary image processor and its application (welding physics, processes & instruments). Transactions of JWRI, Vol. 8, No. 2, 169-174 Mi, L. & Jia, Y.B. (2004). High precision contour tracking with joystick sensor. Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’04), Vol. 1, 804-809, Sendai, Japan, September-October 2004 Oomen, G.L. & Verbeck, W.J.P.A. (1983). A real-time optical profile sensor for robot arc welding. Proceedings of the 3 rd International Conference on Robot Vision and Sensory Controls, 659-668, Cambridge, USA, November 1983 Paul, R. (1979). Manipulator Cartesian path control. IEEE Transactions on Systems, Man and Cybernetics, Vol. 9, No. 11, 702-711, ISSN 0018-9472 Paul, R.P.C. (1972). Modeling, trajectory calculation and servoing of a computer controlled arm. Ph.D. Dissertation, Stanford University, CA., USA Prabuwono, A.S.; Burhanuddin, M.A. & Samsi, M.S. (2008). Autonomous contour tracking using staircase method for industrial robot. Proceeding of the 10 th IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV’08), 2272-2276, Hanoi, Vietnam, December 2008 Prabuwono, A.S. & Samsi, M.S. (2007). Development of adapting gradient method for contour tracking in industrial robot application. Proceeding of the 10 th IASTED International Conference on Intelligent Systems and Control (ISC’07), 592-068, Cambridge, USA, November 2007 Prabuwono, A.S.; Samsi, M.S.; Sulaiman, R. & Sundararajan, E. (2009). Contour following task with dual sensor logic algorithm for Adept Selective Compliant Assembly Robot arm robot. Journal of Computer Science, Vol. 5, No. 8, 557-563, ISSN 1549-3636 Prinze, F.B. & Gunnarson, K.T. (1984). Robotics seam tracking. Interim Report, CMU-RI-TR- 84-10, Carnegie-Mellon University, Pittsburgh, USA Rasol, Z.; Sanders, D.A. & Tewkesbury, G.E. (2001). New prototype knowledge based system to automate a robotics spot welding process. Elektrika, Vol. 4, 28-32 Samsi, M.S. & Nazim, M. (2005). Autonomous and intelligent contour tracking industrial robot. Proceedings of International Conference on Mechatronics, 78-86, Kuala Lumpur, Malaysia, May 2005 PerformanceEvaluationofAutonomousContourFollowingAlgorithmsforIndustrialRobot 397 It is clearly seen in that the staircase method has the longest path followed by the adapting gradient method. The shortest distance is done by the sweeping radius method. With the same speed, it seems that the staircase method takes the longest time while sweeping radius is the fastest of all methods. All the results are tabulated in Table 1. The adapting gradient method consumes medium teaching time at standard deviation value of 2.3085 millimeter, while the staircase method consumes the longest teaching time at standard deviation value of 1.8412 millimeter. The sweeping radius method is very efficient in term of shortest teaching path but its standard deviation value of 3.2663 is a bit high. Criteria Adapting Gradient Staircase Sweeping Radius Mean of Error -0.3773 3.4011 0.2101 Standard Deviation 2.3085 1.8412 3.2663 Path Length Medium Long Shortest Table. 1. Summaries of the results for three different contour following methods. 7. Conclusion In this study, the performance evaluations of autonomous contour following task with three different algorithms have been performed for Adept SCARA robot. A prototype of smart tool integrated with sensor has been designed. It can be attached and reattached into robot gripper and interfaced through I/O pins of Adept robot controller for automated robot teaching operation. The algorithms developed were tested on a semicircle object of 40 millimeter radius. The semicircle object was selected because it exhibits the stringent test bed which provides the changing gradient gradually from steepest positive slope into zero slope of flat curve in the middle and finally to steepest negative slope. The adapting gradient method consumes medium teaching time at reasonable accuracy of standard deviation value of 2.3085 millimeter, while the staircase method consumes the longest teaching time at standard deviation value of 1.8412 millimeter. The sweeping radius method is very efficient in term of shortest teaching path but its standard deviation value of 3.2663 is a bit high. It can be concluded that the staircase method is the most accurate method, while the sweeping radius method has the shortest teaching path. These tests exhibit the performance of algorithms used which prove its possibility to be applied in the real world application. For the future, automatic curve radius determination between straight line segments can be improved by integrating vision system for the automation of top view (X-Y coordinate) edge finding and path planning. The integration of vision system with the present study will improve the automation level of the project from two to three dimensional capabilities. 8. References Adolfo, B.; Sadek, C.A.A. & Leszek, A.D. (2001). Predictive sensor guided robotics manipulators in automated welding cells. Journal of Materials Processing Technology, Vol. 109, No. 1-2, February 2001, 13-19, ISSN 0924-0136 Andersson, J.E. & Johansson, G. (2000). Robot control for wood carving operations. Mechatronics, Vol. 11, No. 4, June 2001, 475-490, ISSN 0957-4158 Awahara, M. & Taki, K. (1979). Tracking control for guiding electrodes along joints by pattern detection of welding groove. Transactions of the Society of Instrument and Control Engineers, Vol. 15, 492 Gopalakrishnan, B.; Tirunellayi, S. & Todkar, R. (2004). Design and development of an autonomous mobile smart vehicle: A mechatronics application. Mechatronics, Vol. 14, No. 5, 491-514, ISSN 0957-4158 Hanright, J. (1984). Selecting your first arc welding robot – a guide to equipment and features. Welding Journal, Vol. 1, 41-45 Hewit, J. (1996). Mechatronics design – the key to performance enhancement. Robotics and Autonomous Systems, 135–142, ISSN 0921-8890 Ikeuchi, K. & Suehiro, T. (1994). Towards an assembly plan from observation, Part I: Task recognition with polyhedral objects. IEEE Transactions on Robotics and Automation, Vol. 10, No. 3, 368-385, ISSN 1042-296X Inoue, K. (1979). Image processing for on-line detection of welding process (report 1): simple binary image processor and its application (welding physics, processes & instruments). Transactions of JWRI, Vol. 8, No. 2, 169-174 Mi, L. & Jia, Y.B. (2004). High precision contour tracking with joystick sensor. Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’04), Vol. 1, 804-809, Sendai, Japan, September-October 2004 Oomen, G.L. & Verbeck, W.J.P.A. (1983). A real-time optical profile sensor for robot arc welding. Proceedings of the 3 rd International Conference on Robot Vision and Sensory Controls, 659-668, Cambridge, USA, November 1983 Paul, R. (1979). Manipulator Cartesian path control. IEEE Transactions on Systems, Man and Cybernetics, Vol. 9, No. 11, 702-711, ISSN 0018-9472 Paul, R.P.C. (1972). Modeling, trajectory calculation and servoing of a computer controlled arm. Ph.D. Dissertation, Stanford University, CA., USA Prabuwono, A.S.; Burhanuddin, M.A. & Samsi, M.S. (2008). Autonomous contour tracking using staircase method for industrial robot. Proceeding of the 10 th IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV’08), 2272-2276, Hanoi, Vietnam, December 2008 Prabuwono, A.S. & Samsi, M.S. (2007). Development of adapting gradient method for contour tracking in industrial robot application. Proceeding of the 10 th IASTED International Conference on Intelligent Systems and Control (ISC’07), 592-068, Cambridge, USA, November 2007 Prabuwono, A.S.; Samsi, M.S.; Sulaiman, R. & Sundararajan, E. (2009). Contour following task with dual sensor logic algorithm for Adept Selective Compliant Assembly Robot arm robot. Journal of Computer Science, Vol. 5, No. 8, 557-563, ISSN 1549-3636 Prinze, F.B. & Gunnarson, K.T. (1984). Robotics seam tracking. Interim Report, CMU-RI-TR- 84-10, Carnegie-Mellon University, Pittsburgh, USA Rasol, Z.; Sanders, D.A. & Tewkesbury, G.E. (2001). New prototype knowledge based system to automate a robotics spot welding process. Elektrika, Vol. 4, 28-32 Samsi, M.S. & Nazim, M. (2005). Autonomous and intelligent contour tracking industrial robot. Proceedings of International Conference on Mechatronics, 78-86, Kuala Lumpur, Malaysia, May 2005 RobotManipulators,TrendsandDevelopment398 Suga, Y.; Takahara, K. & Ikeda, M. (1992). Recognition of weld line and automatic weld line tracking by welding robot with visual and arc voltage sensing system. Journal of the Japan Society for Precision Engineering, 1060-1065 Tomizuka, M.; Dornfield, D. & Purcelli, M. (1980). Applications of microcomputer to automatic weld quality control. ASME Journal of Dynamics Systems, Measurement and Control, 62-68 Yuehong, Y.; Hui, H. & Yanchun, X. (2004). Active tracking of unknown surface using force sensing and control technique for robot. Sensors and Actuators: A Physical, Vol. 112, No. 2-3, 313-319, ISSN 0924-4247 Zollner, R.; Rogalla, O.; Dillmann, R. & Zollner, M. (2002). Understanding users intention: programming fine manipulation tasks by demonstration. Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’02), 1114-1119, Laussane, Switzerland, September-October 2002 AdvancedDynamicPathControloftheThreeLinks SCARAusingAdaptiveNeuroFuzzyInferenceSystem 399 Advanced Dynamic Path Control of the Three Links SCARA using AdaptiveNeuroFuzzyInferenceSystem PrabuD,SurendraKumarandRajendraPrasad X Advanced Dynamic Path Control of the Three Links SCARA using Adaptive Neuro Fuzzy Inference System Prabu D†, Surendra Kumar‡ and Rajendra Prasad‡ Wipro Technologies†, NJ, USA and Indian Institute of Technology‡, Roorkee, India 1. Introduction The very precise control of robot manipulator to track the desired trajectory is a very tedious job and almost unachievable to certain limit with the help of adaptive controllers. This task is achievable to certain limit with the help of adaptive controllers but these controllers also have their own limitation of assuming that the system parameters being controlled change relatively very slow. With reference to the tasks assigned to an industrial robot, one important issue is to determine the motion of the joints and the end effectors of the robot. Therefore, the purpose of the robot arm control, as Fu et al (1987) wrote in one classical works on robotics, is to maintain the dynamic response of the manipulator in accordance with some prespecified performance criterion. Among the early robots of the first generation, non-servo control techniques, such as bang-bang control and sequence control were used. These robots move from one position to another under the control or limit switches, relays, or mechanical stops. During the 1970s, a great deal of work was focused on including such internal state sensors as encoders, potentiometers, tachogenerators, etc., into the robot controller to facilitate manipulative operation ((Inoue, H.,(1974) and Wills, et al (1975)) . Since then, feedback control techniques have been applied for servoing robot manipulators. Up till now, the majority of practical approaches to the industrial robot arm controller design use traditional techniques, such as Proportional and Derivative (PD) or Proportional-Integral-Derivative (PID) controllers, by treating each joint of the manipulator as a simple linear servomechanism. In designing these kinds of controllers, the non-linear, coupled and time-varying dynamics of the mechanical part of the robot manipulator system are completely ignored, or dealt with as disturbances. These methods generally give satisfactory performance when the robot operates at a low speed. However, when the links are moving simultaneously and at a high speed, the non-linear coupling effects and the interaction forces between the manipulator links may degrade the performance of the overall system and increase the tracking errors. The disturbances and uncertainties in a task cycle may also reduce the tracking quality of robot manipulators. Thus, these methods are only suitable for relatively slow manipulator motion and for 18 RobotManipulators,TrendsandDevelopment400 limited-precision tasks can be found in the work by Sciavicco (1996). The Computed Torque Control (CTC) is commonly used in the research community. The CTC law has the ability to make the error asymptotically stable if the dynamics of the robot are exactly known .Paul, R.C (1972). However, manipulators are subject to structured and/or unstructured uncertainty. Structured uncertainty is defined as the case of a correct dynamic model but with parameter uncertainty due to tolerance variances in the manipulator link properties, unknown loads, inaccuracies in the torque constants of the actuators, and others. Unstructured uncertainty describes the case of unmodeled dynamics, which result from the presence of high-frequency modes in the manipulator, neglected time-delays and nonlinear friction. It has been widely recognized that the tracking performance of the CTC method in high-speed operations is severely affected by the structured and unstructured uncertainties. To cope with the problem, some adaptive approaches have been proposed to maintain the tracking performance of the robotic manipulator in the presence of structured uncertainty. Dubowsky(1979). To overcome the above mentioned drawback in manipulator motion control, the chapter proposed a Tuned-ANFIS controller for three links Selective Compliant Articulated Robot Arm (SCARA) manipulators. The proposed Tuned-Adaptive Neuro Fuzzy Inference System (ANFIS) controller is designed to overcome the unmodeled dynamics in the presence of structured and unstructured uncertainties of SCARA. The proposed Tuned-ANFIS Controller combines the advantages of fuzzy and neural network intelligence, which helps to improve the overall learning ability, adaptability of the ANFIS controller and also to achieve robust control of SCARA in unmodeled dynamic control. This Tuned-ANFIS Controller has been applied to the Continuous Path Control of SCARA. The result obtained through the tuned ANFIS is encouraging and shows very good tracking performance. The chapter is structured as follows, Section 2 Overview of SCARA robot control system, Section 3 describes the proposed Adaptive Neuro Fuzzy Inference System and Section 4 presents the ANFIS architecture and learning algorithm and simulation of Continuous Path Motion (CPM) of real-world applications of SCARA Robot Manipulator. Finally, conclusions are summarized in Section5. Prabu D† was a Master of Technology (M.Tech) graduate student in the Department of Electrical Engineering ( with Specialization of System Engineering and Operations Research) of Indian Institute of Technology (IIT) Roorkee, Uttarakhand, 247667, India. This work was done during 2002 through 2004. Currently, He is working with the Wipro Technologies, USA (R&D), Brunswick City, NJ, USA. The proposed book chapter work is not connected with Wipro Technologies, USA. He can be reached for any correspondence of this paper by E-mail: prabud.iitr@gmail.com. He is a member of IEEE, ACM and CMG. Dr Surendra Kumar‡ is a faculty with the Department of Electrical Engineering, IIT Roorkee, Uttarakhand, 247667, India. E-mail: surendra_iitr@yahoo.com. He is a member of IEEE and Chapter President & Director, India Service Region,Olu Olu Institute Consortium for Teaching,Research,Learning & Development, Ruston Louisiana,USA. Dr. Rajendra Prasad‡ is a faculty with the Department of Electrical Engineering, IIT Roorkee, Uttarakhand, 247667, India. E-mail: rpdeefee@iitr.ernet.in. 2. Overview of SCARA Robot Control System The SCARA acronym stands for Selective Compliant Assembly Robot Arm or Selective Compliant Articulated Robot Arm. SCARA is normally used in industries for pick and place operation, etc. Fig. 1. Shows the SCARA Robot The figure 1 shows the model picture of SCARA with two vertical revolute joint and one vertical prismatic joint used in this experiment. In this experiment, the dynamical model of SCARA robot is derived using Newton Euler formulation is used for simulating the CPM control using ANFIS and PD Controller. Robot Manipulator control action are exercised in the joint co-ordinates. Moreover, the dynamical model of the three links SCARA is given in many robotics books and papers. The figure 2 shows the basic ANFIS feedback control system for the CPM control of SCARA Manipulator used in this experiment. Fig. 2. Shows the ANFIS feedback control system for Continuous Path Motion control of SCARA [...]... (t), q2 (t) = 0.5sin (t)) and joint distance (q3 (t) = 0.3m) 408 Robot Manipulators, Trends and Development 4.1 Continuous Path Control & Experimental Results The Continuous Path Motion (CPM), sometimes called controlled-path motion, Schilling (1990) Normally SCARA’s are used for pick and place applications in many industries The positioning and controlling of SCARA End effectors and manipulator are more... given in many robotics books and papers The figure 2 shows the basic ANFIS feedback control system for the CPM control of SCARA Manipulator used in this experiment Fig 2 Shows the ANFIS feedback control system for Continuous Path Motion control of SCARA 402 Robot Manipulators, Trends and Development The feedback control system consists of ANFIS controller, servo actuating system for the SCARA Robot manipulator... S Wang, C S G Lee, and C H Juang (1999) Structure and Learning in Self-Adaptive Neural Fuzzy Inference System, Proc of the Eighth Int'l Fuzzy Syst Association World Conf., Taipei, Taiwan, 935-980, August 13-20 Will P and Grossnlan D (1975) An Experimental System for Computer Controlled Mechanical Assembly IEEE Trans EICYY Devices, Vol 29, 42-48 412 Robot Manipulators, Trends and Development Topological... regions remains equal to one, by virtue of corollary 1 There exists only one island above the water As soon as maximum M2  is reached, another island appears and a new disjoint region of Ca is generated The number of disjoint regions remains equal to two until the saddle point S is reached 418 Robot Manipulators, Trends and Development  Consider a point P of Ca , with a contained in the open interval... S’ be two reference frames, attached to the base and to the platform respectively, and with the origin in the centre of the spherical joint between the platform and the base Let the three points P1, P2, and P3 be the centres of the joints between the base and the legs, and the three points Q1, Q2, and Q3 be the centers of the joints between the platform and the legs The kinematic architecture of any... (10) to Eq. (11) Such 424 Robot Manipulators, Trends and Development extraneous solutions are obtained when e0 or e1 are posed equal to zero If e0  0 , Eq. (11) becomes:  J / e0   0  J / e0   0  J / e3  e1   J / e1  e3  0 (12) where the first two equations degenerate into the same one Therefore, Eq.(12) is a set of two homogeneous equations, the first of degree three and the second... References H R Berenji and P Khedkar (1992) Learning and Tuning Fuzzy Logic Controllers through Reinforcements, IEEE Trans Neural Networks, vol.3, no.5, pp.322-320 Dubowsky S and Desforges, D.T (1979) The application of Model Referenced Adaptive control to Robotic Manipulators, Transactions of ASME, Journal of Dynamic System Vol 101, 193-200 Fu, K.S.; Gonzalez, R.C and Lee, C.S.G., (1987) Robotics: Control,... and Servoing of Computer Controlled Arm, A.I.Memo 177, Stanford Artificial Intelligence Lab., Stanford University California Rong-Jong Wai (2003) Tracking control based on neural network strategy for robot manipulator, Elsevier, Journal of Neuro computing Vol.51, 425- 445 R.J Schilling (1990) Fundamentals of Robotics, Prentice-Hall Sciavicco L and Siciliano B (1996) Modelling and Control of Robot Manipulators. .. with error tolerance of 0 and the performance Mean Square Error (MSE) is found to be 0.0064759 Figure 9 shows the fuzzy rule viewer of MATLAB, which is used for predetermine the output of the model for specific input values 406 Robot Manipulators, Trends and Development Fig 7 Loading Training data for ANFIS structure Fig 8 Training when error tolerance is chosen to be 0 and number of epochs is limited... parallel manipulators can reach higher stiffness and load-bearing capability than serial manipulators of equivalent weight This feature has made them attractive for many applications, including high-precision machining tools, space robots and high-speed manipulators Unfortunately, the drawback of parallel architectures is the more entangled kinematics, which causes many a problem during design and operation . SCARA Robot Manipulators, Trends and Development4 02 The feedback control system consists of ANFIS controller, servo actuating system for the SCARA Robot manipulator system and the SCARA robot. Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’02), 111 4 -111 9, Laussane, Switzerland, September-October 2002 AdvancedDynamicPathControloftheThreeLinks SCARAusingAdaptiveNeuroFuzzyInferenceSystem. Autonomous and intelligent contour tracking industrial robot. Proceedings of International Conference on Mechatronics, 78-86, Kuala Lumpur, Malaysia, May 2005 Robot Manipulators, Trends and Development3 98