A Novel Obstacle-Avoidance Solution With Non-Iterative Neural Controller for Joint-Constrained Redundant Manipulators

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 sampled QP problem using only one iteration, yet no solution is suitable for OA and JLA due to the absence of some derivative information. To tackle these issues, this paper proposes a novel solution with a non-iterative neural controller termed NCP-ZNN for joint-constrained redundant manipulators. Unlike iterative methods, the neural controller involving derivative information proposed in this paper possesses some positive features including non-iterative computing and convergence with time. In this paper, the reestablished OA-JLA scheme is first introduced. Then, the design details of the neural controller are presented. After that, some comparative simulations based on a PA10 robot and an experiment based on a Franka Emika Panda robot are conducted, demonstrating that the proposed neural controller is more competent in OA and JLA.

Authors

• Weibing Li
• Zilian Yi
• Yanying Zou
• Haimei Wu
• Yang Yang
• Yongping Pan 0001

Keywords

• Computational modeling
• Neural networks
• Manipulators
• Mathematical models
• Iterative methods
• Reliability
• Quadratic programming
• Neural Control
• Obstacle Avoidance
• Neural Network
• Re-establish
• Quadratic Programming Problem
• Upper Limit
• Computation Time
• Minimum Distance
• Recurrent Neural Network
• Inequality Constraints
• Path Planning
• Joint Angles
• Ant Colony
• Error Tolerance
• Maximum Iteration
• Robot Manipulator
• Task Duration
• Ant Colony Optimization
• Joint Velocity
• Discrete Control
• Safety Threshold
• Joint Limits
• Path Tracking
• Dynamic Obstacles
• Physical Experiments
• Position Error
• Maximum Absolute Error
• Approximate Optimal Solution