By Topic

Evolutionary multi-objective optimization in robot soccer system for education

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

4 Author(s)

As the robot soccer system becomes stabilized, it has been used as an educational platform with which various topics on mobile robotics can be taught. As one of key topics in the education of mobile robotics is computational intelligence-based navigation, this paper proposes a multiobjective population-based incremental learning (MOPBIL) algorithm to obtain the fuzzy path planner for optimal path to the ball, minimizing three objectives such as elapsed time, heading direction and posture angle errors in a robot soccer system. MOPBIL employs the probabilistic mechanism, which generates new population using probability vectors. As the probability vectors are updated by referring to nondominated solutions, population converges to Pareto-optimal solution set. Simulation and experiment results show the effectiveness of the proposed MOPBIL from the viewpoint of the proximity to the Pareto-optimal set, size of the dominated space, coverage of two sets and diversity metric. By implementing each of the solutions into the educational platform, it can be educated how multi-objective optimization is realized in the real-world problem.

Published in:

Computational Intelligence Magazine, IEEE  (Volume:4 ,  Issue: 1 )