A Novel Obstacle-Avoidance Solution With Non-Iterative Neural Controller for Joint-Constrained Redundant Manipulators
Weibing Li,Zilian Yi,Yanying Zou,Haimei Wu,Yang Yang,Yongping Pan,Weibing Li,Zilian Yi,Yanying Zou,Haimei Wu,Yang Yang,Yongping Pan
Obstacle avoidance (OA) and joint-limit avoidance (JLA) are essential for redundant manipulators to ensure safe and reliable robotic operations. One solution to OA and JLA is to incorporate the involved constraints into a quadratic programming (QP), by solving which OA and JLA can be achieved. There exist a few non-iterative solvers such as zeroing neural networks (ZNNs), which can solve each samp...
