Neural estimator-based finite-time formation control for manipulator end effectors with obstacle avoidance

Key Points

• Formation control improves efficiency and safety in robotic systems with end effectors, particularly in challenging environments.
• Key findings show that the proposed neural estimator successfully reduces time to reach stable formations under obstacles.
• Approach involved developing a neural estimator framework to manage formation control and obstacle avoidance in real-time applications.
• Significance emphasizes the need for adaptive control mechanisms in robotic manipulation, especially for dynamic environments.