Abstract
It has been shown that one can guarantee a reachable workspace for a kinematically redundant robot after an arbitrary locked-joint failure if one artificially restricts the range of its joints prior to the failure. This work presents an algorithm for computing the optimal kinematic parameters and artificial joint limits for a robot to maximize this so-called “failure-tolerant workspace”. The proposed technique employs a genetic algorithm that incorporates a novel method for selecting an initial population that results in fast convergence to high-quality solutions. The algorithm is illustrated on multiple examples of kinematically redundant robots and is shown to be computationally tractable even for robots that perform tasks in 6D workspaces.
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.
