Abstract
Motion planning is an essential task for humanoid robots. However, it is still very challenging to obtain good motion performance in humanoid motion planning, because of its high DOFs (degree of freedoms), variable mechanical structure and nonlinearity. In humanoid motion planning, the motion performance can be given only after one whole cycle motion is completed. This is a demanding condition for motion planning on either real robots or simulation platform. In this paper, a DFNN (dynamic neural fuzzy network) is adopted to model humanoid robots for motion planning. The inputs of DFNN are parameters which determine motion of humanoid robots. The output is evaluation of humanoid motion performance. The DFNN after training can give evaluation of motion performance immediately once the parameters are determined. The DFNN models not only the dynamics of robots, also the motion planning method. Therefore, the DFNN stores two kind of knowledge: the mapping between parameters and humanoid motion, the mapping between humanoid motion and motion performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
