By Topic

Fuzzy critic for robotic motion planning by genetic algorithm in hierarchical intelligent control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shibata, T. ; Robotics Dept., MITI, Tsukuba, Japan ; Fukuda, T. ; Tanie, K.

In this paper, a new strategy for motion planning in robotics is proposed. When robots performs some tasks, they work along with the motion plans. The plane should be effective. The proposed strategy applies a genetic algorithm (GA) to optimize the plans. To evaluate the planned motion, the strategy applies fuzzy logic as a fitness function. The fitness function is referred to as fuzzy critic. The fuzzy critic evaluates populations in the GA with respect to multiple factors. Depending on the goals of the tasks, human operators can easily transfer inference rules in the fuzzy critic because of the fuzzy logic. In this paper, the strategy determines a path for a mobile robot which moves from a starting point to a goal point while avoiding obstacles in a work space and picking up loads on the way. Simulations illustrate the effectiveness of the proposed strategy.

Published in:

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on  (Volume:1 )

Date of Conference:

25-29 Oct. 1993