%0 Conference Paper %1 Homaifar:1999:CIRA %A Homaifar, Abdollah %A Battle, Daryl %A Tunstel, Edward %B Computational Intelligence in Robotics and Automation, CIRA '99. Proceedings. 1999 IEEE International Symposium on %D 1999 %K Darwinian algorithms, automatic autonomous bases, computation, computing-based concepts, control design, evolutionary forward functions, fuzzy genetic learning, mappings, measurement membership mobile noise, nominal nonlinear path problem, programming, robot robot, robustness, rule rules, sensor soft steering tracking, vehicle, velocity %P 35--40 %T Soft computing-based design and control for mobile robot path tracking %U http://ieeexplore.ieee.org/iel5/6589/17587/00809943.pdf?isNumber=17587 %X A variety of evolutionary algorithms, operating according to Darwinian concepts, have been proposed to approximately solve problems of common engineering applications. Increasingly common applications involve automatic learning of nonlinear mappings that govern the behavior of control systems. In many cases where robot control is of primary concern, the systems used to demonstrate the effectiveness of evolutionary algorithms often do not represent practical robotic systems. In this paper, genetic programming (GP) is the evolutionary strategy of interest. It is applied to learn fuzzy control rules for a practical autonomous vehicle steering control problem, namely, path tracking. GP handles the simultaneous evolution of membership functions and rule bases for the fuzzy path tracker. As a matter of practicality, robustness of the genetically evolved fuzzy controller is demonstrated by examining the effects of sensor measurement noise and an increase in the robot's nominal forward velocity. %@ 0-7803-5806-6

@inproceedings{Homaifar:1999:CIRA, abstract = {A variety of evolutionary algorithms, operating according to Darwinian concepts, have been proposed to approximately solve problems of common engineering applications. Increasingly common applications involve automatic learning of nonlinear mappings that govern the behavior of control systems. In many cases where robot control is of primary concern, the systems used to demonstrate the effectiveness of evolutionary algorithms often do not represent practical robotic systems. In this paper, genetic programming (GP) is the evolutionary strategy of interest. It is applied to learn fuzzy control rules for a practical autonomous vehicle steering control problem, namely, path tracking. GP handles the simultaneous evolution of membership functions and rule bases for the fuzzy path tracker. As a matter of practicality, robustness of the genetically evolved fuzzy controller is demonstrated by examining the effects of sensor measurement noise and an increase in the robot's nominal forward velocity.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Homaifar, Abdollah and Battle, Daryl and Tunstel, Edward}, biburl = {https://www.bibsonomy.org/bibtex/229deaa5a5c13370768a56e50cd31144b/brazovayeye}, booktitle = {Computational Intelligence in Robotics and Automation, CIRA '99. Proceedings. 1999 IEEE International Symposium on}, interhash = {00228a1fb368a1ecb3373368b19ac118}, intrahash = {29deaa5a5c13370768a56e50cd31144b}, isbn = {0-7803-5806-6}, keywords = {Darwinian algorithms, automatic autonomous bases, computation, computing-based concepts, control design, evolutionary forward functions, fuzzy genetic learning, mappings, measurement membership mobile noise, nominal nonlinear path problem, programming, robot robot, robustness, rule rules, sensor soft steering tracking, vehicle, velocity}, month = {8-9 November}, notes = {CIRA'99 http://web.nps.navy.mil/~yun/cira99/}, pages = {35--40}, size = {6 pages}, timestamp = {2008-06-19T17:41:38.000+0200}, title = {Soft computing-based design and control for mobile robot path tracking}, url = {http://ieeexplore.ieee.org/iel5/6589/17587/00809943.pdf?isNumber=17587}, year = 1999 }