Fuzzy-Genetic Optimal Control for Four Degreeof Freedom Robotic Arm Movement

Authors: V. K. Banga, R. Kumar, Y. Singh

Abstract:

In this paper, we present optimal control for movement and trajectory planning for four degrees-of-freedom robot using Fuzzy Logic (FL) and Genetic Algorithms (GAs). We have evaluated using Fuzzy Logic (FL) and Genetic Algorithms (GAs) for four degree-of-freedom (4 DOF) robotics arm, Uncertainties like; Movement, Friction and Settling Time in robotic arm movement have been compensated using Fuzzy logic and Genetic Algorithms. The development of a fuzzy genetic optimization algorithm is presented and discussed. The result are compared only GA and Fuzzy GA. This paper describes genetic algorithms, which is designed to optimize robot movement and trajectory. Though the model represents is a general model for redundant structures and could represent any n-link structures. The result is a complete trajectory planning with Fuzzy logic and Genetic algorithms demonstrating the flexibility of this technique of artificial intelligence.

Keywords: Inverse kinematics, Genetic algorithms (GAs), Fuzzy logic (FL), Trajectory planning.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057761

References:

[1] Edward T. Lee, "Applying fuzzy logic to robot navigation", Journal of Kybernetes, 24(6), 38-43(1995).
[2] Monteiro D. C., Madrid M. K., "Planning of robot trajectories with genetic algorithms", IEEE Proceedings of the first workshop on Robot Motion and Control, RoMoCo '99, Kiekrz, Poland, 223-228 (1999).
[3] Olson C. F., J. P. Lab., Technol. CIo, Pasadena "Probabilistic selflocalization for mobile robots", IEEE Transactions on Robotics and Automation, Vol.16, No.1, 55-66(2000).
[4] Gillespie R. B., Colgate J. E., Peshkin M. A. "A general framework for robot control", IEEE Transactions on Robotics and Automation, Vol.17, No.4, 391-401(2001).
[5] Devendra P. Garg and Manish kumar, "Optimization Techniques applied to multiple manipulators for path planning and torque minimization", Engineering Applications of Artificial Intelligence, Vol. 15, No. 3-4, 241-252 (2002).
[6] Nasser Sadati, Javid Taheri, "Genetic algorithm in robot path planning problem in crisp and fuzzified environments", IEEE ICiT-02, Bangkok, Thailand, 175-180 (2002).
[7] Gemeinder M. and Gerke M., "GA-based Path Planning for Mobile Robot Systems employing an active Search Algorithm", Journal of Applied Soft Computing, Vol. 3, No. 2, 149-158 (2003).
[8] Rosales E. M., Gan J. Q., Huosheng Hu, Oyama E., "A hybrid appproach to inverse kinematics modelling and control of pioneer 2 robotic arms", Technical report CSM-413, University of Essex (2003).
[9] Galantucci L. M., Percoco G., Spina R., "Assembly and disassembly planning by using fuzzy logic & genetic algorithms", International Journal of Advanced Robotic Systems, 1(2), 67-74 (2004).
[10] Zacharia P. Th. and Aspragathos N. A., "Optimal Robot task scheduling based on Genetic Algorithms", Elsevier Robotics and Computer- Integrated Manufacturing, Vol. 21, 67-79 (2005).
[11] Ho H. F. at. el. , "Robust fuzzy tracking control for robotic manipulators" , Simulation Modelling Practice and Theory , Vol. 15, Issue 7, 801-816 (2007).
[12] Nguyen . V. B. and Morris A. S., "Genetic Algorithm Tuned Fuzzy Logic Controller for a Robot Arm with Two-link Flexibility and Twojoint Elasticity", Springer J Intell Robot Syst, Vol. 49, 3-18(2007).
[13] M. Mucientes et.el. , "Design of a fuzzy controller in mobile robotics using genetic algorithms", Elsevier Applied Soft Computing, Vol. 7, No. 2, 540-546 (2007).
[14] Alam M. S., Tokhi M. O., "Hybrid fuzzy logic control with genetic optimisation for a single-link flexible manipulator" Elsevier Engineering Applications of Artificial Intelligence, 21(6), 858-873 (2008).
[15] Momotaz Begum, George K. I. Mann, Raymond G. Gosai, "Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots", Elsevier Applied Soft Computing 8(1), 50-165 (2008).