Modeling and Simulation of Robotic Arm Movement using Soft Computing

In this research paper we have presented control architecture for robotic arm movement and trajectory planning using Fuzzy Logic (FL) and Genetic Algorithms (GAs). This architecture is used to compensate the uncertainties like; movement, friction and settling time in robotic arm movement. The genetic algorithms and fuzzy logic is used to meet the objective of optimal control movement of robotic arm. This proposed technique represents a general model for redundant structures and may extend to other structures. Results show optimal angular movement of joints as result of evolutionary process. This technique has edge over the other techniques as minimum mathematics complexity used.




References:
[1] C. F. Olson, Lab. JP, Technol. CIo, Pasadena, "Probabilistic selflocalization
for mobile robots"; IEEE Transactions on Robotics and
Automation 16,1, 55-66, 2000.
[2] R.B. Gillespie, J. E. Colgate, M. A. Peshkin, "A general framework for
robot control"; IEEE Transactions on Robotics and Automation, 17,4,
391-401, 2001.
[3] Devendra P. Garg and Manish Kumar, "Optimization Techniques
applied to multiple manipulators for path planning and torque
minimization"; Engineering Applications of Artificial Intelligence 15, 3-
4, 241-252, 2002.
[4] J. C. Trinkle and R. James Milgram, "Complete Path Planning for
Closed Kinematics Chains with Spherical Joints"; SAGE International
Journal of Robotic Research 21, 9, 773-789, 2002.
[5] M. Gemeinder and M. Gerke, "GA-based Path Planning for Mobile
Robot Systems employing an active Search Algorithm"; Journal of
Applied Soft Computing 3, 2, 149-158, 2003.
[6] P. Th. Zacharia and N. A. Aspragathos, "Optimal Robot task scheduling
based on Genetic Algorithms"; Elsevier Robotics and Computer-
Integrated Manufacturing 21, 67-79, 2005.
[7] V. B. Nguyen and A. S. Morris, "Genetic Algorithm Tuned Fuzzy Logic
Controller for a Robot Arm with Two-link Flexibility and Two-joint
Elasticity"; Springer J Intell Robot Syst. 49, 3-18, 2007.
[8] M. Mucientes, D. L. Moreno, "A. Bugarín and S. Barro, Design of a
fuzzy controller in mobile robotics using genetic algorithms"; Elsevier
Applied Soft Computing 7, 2, 540-546, 2007.
[9] 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.
[10] L. Doitsidis, N. C. Tsourveloudis, S. Piperidis, "Evolution of Fuzzy
Controllers for Robotic Vehicles: The Role of Fitness Function
Selection"; Springer J. Intell. Robot Syst. 56, 469-484, 2009.