Abstract
Redundant manipulators have been widely used in various industries whose applications not only improve production efficiency and reduce manual labor but also promote innovation in robotics and artificial intelligence. Kinematic control plays a fundamental and crucial role in robot control. Over the past few decades, numerous motion control schemes have been proposed and applied to trajectory tracking tasks. However, most of these schemes do not consider the introduction of sparsity into the motion control of redundant manipulators, resulting in excessive joint movements, which not only consume extra energy but also increase the risk of unexpected collisions in complex environments. To solve this problem, we transform the issue of increasing the sparsity into a nonconvex optimization problem. Furthermore, a collective neural dynamics for sparse motion planning (CNDSMP) scheme for motion planning of redundant manipulators is proposed. By incorporating sparsity into the control scheme, the excessive joint movements are minimized, leading to improved efficiency and reduced collision risks. Through simulations, comparisons, and physical experiments, the effectiveness and superiority of the proposed scheme are demonstrated.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE transactions on neural networks and learning systems
Disclaimer: 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.