Abstract
Kinematically-redundant manipulators present considerable difficulties, especially from the view of control. A high number of degrees of freedom are used to control so-called secondary tasks in order to optimize manipulator motion. This paper introduces a new algorithm for the control of kinematically-redundant manipulator considering three secondary tasks, namely a joint limit avoidance task, a kinematic singularities avoidance task, and an obstacle avoidance task. For path planning of end-effector from start to goal point, the potential field method is used. The final inverse kinematic model is designed by a Jacobian-based method considering weight matrices in order to prioritize particular tasks. Our approach is based on the flexible behavior of priority value due to the acceleration of numerical simulation. The results of the simulations show the advantage of our approach, which results in a significant decrease of computing time.
Highlights
Kinematically-redundant manipulators are mechanisms which have more degrees of freedom (DOF) than is required for the execution of a given task
This study investigates the algorithms for investigating the positioning of manipulator end-effector while secondary tasks are considered, namely a joint limit avoidance task, an obstacle avoidance task, and a kinematic singularities avoidance task
The paper deals with path planning for end-effector using potential field method
Summary
Kinematically-redundant manipulators are mechanisms which have more degrees of freedom (DOF) than is required for the execution of a given task. The advantage of kinematically-redundant manipulators in comparison with conventional manipulators is in the utilization of redundant manipulator joints for optimization tasks [1,2]. These optimization tasks are secondary tasks of the inverse kinematic or dynamic model. Kinematic redundancy causes disadvantages, such as the requirements of greater structural complexity of manipulator construction (higher number of actuators, sensors, costs, etc.). It is important to note that control algorithms for inverse kinematic and dynamic model are considerably more complicated [5]
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