Redundant Manipulator

Redundant manipulators, robots with more joints than strictly necessary for task completion, are studied to leverage their extra degrees of freedom for improved dexterity, obstacle avoidance, and optimized movement. Current research focuses on efficient inverse kinematics (IK) solutions, employing techniques like quadratic programming, stochastic approximation, and machine learning (neural networks) to find optimal joint configurations that satisfy task requirements while considering factors such as joint efficiency, collision avoidance, and manipulability. These advancements are significant for improving robot control and planning, particularly in complex environments and dynamic tasks, leading to more robust and adaptable robotic systems across various applications.