Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Obstacle avoidance for kinematically redundant manipulators using a dual neural network.

Yunong Zhang1, Jun Wang

  • 1Department of Automation and Computer-Aided Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 17, 2004
PubMed
Summary

This study introduces a novel recurrent neural network for redundant manipulators, enabling effective obstacle avoidance during motion planning. The approach integrates dynamic constraints for collision-free path generation.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Synthesis and micellization of amphiphilic brush-coil block copolymer based on poly(epsilon-caprolactone) and PEGylated polyphosphoester.

Biomacromolecules·2006
Same author

Intravenous transplantation of mesenchymal stem cells improves cardiac performance after acute myocardial ischemia in female rats.

Transplant international : official journal of the European Society for Organ Transplantation·2006
Same author

[Effects of mechanical tensile stress on the expression of ICAM-1 mRNA in osteoblasts differentiated from rBMSCs].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2006
Same author

[Effects of osteoporosis on experimental tooth movement in aged rats].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2006
Same author

MCALIGN2: faster, accurate global pairwise alignment of non-coding DNA sequences based on explicit models of indel evolution.

BMC bioinformatics·2006
Same author

[Managements of masked mastoiditis].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2006

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Obstacle avoidance is a critical challenge in motion planning for kinematically redundant manipulators.
  • Existing methods often struggle with dynamic environments and incorporating physical limitations.

Purpose of the Study:

  • To develop a recurrent neural network for kinematic control of redundant manipulators with enhanced obstacle avoidance.
  • To incorporate dynamic inequality constraints for collision avoidance and physical joint limits into the control formulation.

Main Methods:

  • A dual neural network architecture was developed for online solution of the collision-free inverse kinematics problem.
  • The formulation dynamically updates inequality constraints to represent collision avoidance requirements.

Related Experiment Videos

  • Physical joint limits were directly integrated into the problem formulation.

    • Main Results:

    • The proposed neural network demonstrated effective motion control for the PA10 robot arm.
    • Successful obstacle avoidance was achieved in the presence of both point and window-shaped obstacles.
    • The method efficiently handles dynamically updated constraints for real-time applications.

    Conclusions:

    • The developed recurrent neural network provides a robust solution for kinematically redundant manipulator control with obstacle avoidance.
    • The integration of dynamic and physical constraints enhances the safety and applicability of robotic motion planning.
    • This approach offers a promising direction for real-time, collision-free robotic motion in complex environments.