By Topic

Evolutionary Multivalued Decision Diagrams for Obtaining Motion Representation of Humanoid Robots

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)
Sakai, M. ; Grad. Sch. of Eng., Nagoya Inst. of Technol., Nagoya, Japan ; Kanoh, M. ; Nakamura, T.

In this paper, we propose a method, using multivalued decision diagrams (MDDs), to obtain motion representation of humanoid robots. Kanoh et al. have proposed a method, which uses multiterminal binary decision diagrams (MTBDDs), to acquire robot controller. However, nonterminal vertices of MTBDDs can only treat values of 0 or 1; multiple variables are needed to represent a single joint angle. This increases the number of the non-terminal vertices and MTBDDs that represent that the controller becomes complex. Therefore, we consider using the MDD, in which its nonterminal vertices can take on multiple output values. To obtain humanoid robot motion representation, we propose evolutionary MDDs and show experimental results comparing evolutionary MDDs and evolutionary MTBDDs through simulations of acquisition of robot motion in this paper. Moreover, we verify whether the evolution of MDD using a memetic algorithm is effective.

Published in:

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:42 ,  Issue: 5 )