Real-Time-Position Prediction Algorithm for Under-Actuated Robot Manipulator Using of Artificial Neural Network

Robot manipulators, in general, are required to have the same number of actuators as the number of joints to obtain full control. In the case of under-actuated robots, this condition is not satisfied which make the behavior of that class of robots very difficult to be predicted. Under-actuated robots can be a better design choice for robots in space and other industrial applications, their advantages over fully actuated robots led to many studies to predict their behavior (Yu et al., 1998; Berkemeier & Fearing, 1999; Spong, 1995; Ono et al., 2001; Nakanishi et al., 2000; Funda et al., 1996; Luca et al., 2000; Luca & Oriolo, 2002; Arai & Tachi, 1991; Mukherjee & Chen, 1993;Yu et al., 1993;Bergerman et al., 1995; Mahindrakar et al., 2006; Muscato, 2006; Begovich et al., 2002). As a first advantage, a light-weight and low power consumption manipulator can be made. This feature is required in low cost automation and space robots. Second, they can easily overcome actuator failure due to unexpected accident. The under-actuated manipulator could be the model of the direct drive manipulator that has some failed joints; such fault-tolerant behavior is highly desirable for robots in remote or hazardous environments (Yu et al., 1998). Other interesting applications include the Acrobot (Berkemeier & Fearing, 1999; Spong, 1995), the gymnast robots (Ono et al., 2001), the brachiating robots (Nakanishi et al., 2000), and surgical robots (Funda et al., 1996). The mathematical complexity and wide variety of applications have kept under-actuated robots an area of open research. (Luca et al., 2000; Luca & Oriolo, 2002) have investigated the behavior of a 2R manipulator moving in a horizontal plane with a single actuator at the first joint, neglecting joint friction which is not easy to achieve in real world as it involves high manufacturing cost. Trying to overcome that problem, some researchers have implemented additional equipments such as breaks at the passive joint (Arai & Tachi, 1991; Mukherjee & Chen, 1993; Yu et al., 1993; Bergerman et al., 1995). In this case, the brake can generate torque that means after all that kind of systems is considered some kind of actuator. So, it will be difficult to consider that robot as an under-actuated manipulator. Motivated by this problem, (Yu et al., 1998) have investigated the dynamic characteristics of a two-link manipulator in view of global motion including joint friction by proposing a mathematical model; they have found that the manipulator can be positioned if the friction

[1]  Subramaniam Balakrishnan,et al.  Intelligent robotic assembly , 2000 .

[2]  Ishak Aris,et al.  An adaptive learning algorithm for controlling a two-degree-of-freedom serial ball-and-socket actuator , 2007 .

[3]  Degang Chen,et al.  Control of free-flying underactuated space manipulators to equilibrium manifolds , 1993, IEEE Trans. Robotics Autom..

[4]  S. Ehsan Shafiei Advanced Strategies for Robot Manipulators , 2010 .

[5]  Alessandro De Luca,et al.  Stabilization of an underactuated planar 2R manipulator , 2000 .

[6]  You-liang Gu,et al.  A fuzzy learning algorithm for kinematic control of a robotic system , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[7]  H. M. A. A. Al-Assadi,et al.  An adaptive-learning algorithm to solve the inverse kinematics problem of a 6 D.O.F serial robot manipulator , 2006, Adv. Eng. Softw..

[8]  Marco H. Terra,et al.  Neural network-based H∞ control for fully actuated and underactuated cooperative manipulators , 2009 .

[9]  Koji Yamamoto,et al.  Control of giant swing motion of a two-link horizontal bar gymnastic robot , 2001, Adv. Robotics.

[10]  Abdel Magid Hamouda,et al.  A new adaptive learning algorithm for robot manipulator control , 2007 .

[11]  Rasit Köker,et al.  Reliability-based approach to the inverse kinematics solution of robots using Elman's networks , 2005, Eng. Appl. Artif. Intell..

[12]  Hikaru Inooka,et al.  Position control of an underactuated manipulator using joint friction , 1998 .

[13]  Edgar N. Sánchez,et al.  Takagi-Sugeno fuzzy scheme for real-time trajectory tracking of an underactuated robot , 2002, IEEE Trans. Control. Syst. Technol..

[14]  Stanislaw H. Zak,et al.  Designing a genetic neural fuzzy antilock-brake-system controller , 2002, IEEE Trans. Evol. Comput..

[15]  Prasad K. Yarlagadda,et al.  A study on prediction of bead height in robotic arc welding using a neural network , 2002 .

[16]  Ishak Aris,et al.  Artificial neural network-based kinematics Jacobian solution for serial manipulator passing through singular configurations , 2010, Adv. Eng. Softw..

[17]  Yangsheng Xu,et al.  Experimental study of an underactuated manipulator , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[18]  G. Muscato Fuzzy control of an underactuated robot with a fuzzy microcontroller , 1999, Microprocess. Microsystems.

[19]  Ishak Aris,et al.  Trajectory tracking for a serial robot manipulator passing through singular configurations based on the adaptive kinematics Jacobian method , 2009 .

[20]  Giuseppe Oriolo,et al.  Trajectory Planning and Control for Planar Robots with Passive Last Joint , 2002, Int. J. Robotics Res..

[21]  Ravi N. Banavar,et al.  Point-to-point control of a 2R planar horizontal underactuated manipulator , 2006 .

[22]  W. Chambers San Antonio, Texas , 1940 .

[23]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[24]  Hikaru Inooka,et al.  Dynamics and motion control of a two-link robot manipulator with a passive joint , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[25]  Susumu Tachi,et al.  Position control of manipulator with passive joints using dynamic coupling , 1991, IEEE Trans. Robotics Autom..

[26]  Jun Nakanishi,et al.  A brachiating robot controller , 2000, IEEE Trans. Robotics Autom..

[27]  Russell H. Taylor,et al.  Constrained Cartesian motion control for teleoperated surgical robots , 1996, IEEE Trans. Robotics Autom..

[28]  KökerRaşit Reliability-based approach to the inverse kinematics solution of robots using Elman's networks , 2005 .

[29]  Mark W. Spong,et al.  The swing up control problem for the Acrobot , 1995 .