Global task-space adaptive control of robot

Task-space feedback information such as visual feedback is used in many modern robot control systems as it improves robustness to model uncertainty. However, existing task-space feedback control schemes are only valid locally in a finite task space within a limited sensing zone where the singularity of the Jacobian matrix can be avoided. The global stability problem of a task-space control system has not been systemically solved. In this paper, we introduce a novel regional feedback method for robot task-space control. Each feedback information is employed in a local region, and the combination of regional information ensures the global convergence of robot motion. The transition from one feedback information to another is embedded in the controllers without using any hard or discontinuous switching. Using the regional feedback, a new task-space control method is proposed, which consists of a reaching task variable that drives the robot from one task space to another and a desired task variable to move the robot to the desired position at the ending stage. We shall show that the proposed regional feedback control method is a united formulation to address various open issues in task-space control problems such as singularity problem and limited sensing zone. This is the first result in task-space control that the global dynamic stability can be guaranteed with the consideration of singularity issues and limited sensing zones.