Self-Adjusting Active Compliance Controller For Two Cooperating Robots Handling A Flexible Object

In this paper a self-adjusting control strategy for two motion servoed robots handling a flexible object is presented. The control strategy consists of a feedforward and feedback level. The feedforward level contains motion coordination, force distribution of external forces, creation of internal forces, and an additional loop adding the elastic displacements due to the applied forces to the planned robot positions. The feedback level is constructed as an active compliance control law. For adjusting the controller to the in general unknown flexible behaviour a procedure is presented, capable of determining the compliance of the considered system. The performance of the proposed scheme is tested by simulation

Experimental Validation of a Self-Adjusting Active Compliance Controller for Multiple Robots Handling an Object

This paper presents the experimental validation of a self-adjusting active compliance controller for n robots handling a concerning its compliant behaviour partly unknown flexible object. The control strategy is based on the decomposition of the 6n-dimensional position/force space and includes a feedforward and feedback level. For adjusting the controller to the in general unknown flexible behaviour, which is the main problem of the controller design, a quasi-static model of the system is derived for different contact cases of the object and a procedure is presented, which by use of this model is capable of determining the compliance of the considered system and therefore of adjusting the controller. Experiments with two pumatype robots show the applicability of the self-adjusting control strategy

Learning Approach to the Active Compliance Control of Multi-Arm Robots Coupled through a Flexible Object

This paper presents a quasi-static model and a control strategy for N robot arms cooperating through a concerning its compliant behaviour partly unknown flexible object. The control strategy is based on the position/force decomposition of an extended 6N-dimensional space. The strategy includes feedforward and feedback levels. The Feedback level is organized in the form of an active compliance control law. An AMS-based learning approach is used to accommodate the compliance behaviour of the system and utilized as an additional feedforward loop in the control system. The applicability of the control strategy is verified by simulation

Learning approach to the active compliance control of multi-arm robots coupled through a flexible object

This paper presents a quasi-static model and a control strategy for N robot arms cooperating through a concerning its compliant behaviour partly unknown flexible object. The control strategy is based on the position/force decomposition of an extended 6N-dimensional space. The strategy includes feedforward and feedback levels. The feedback level is organized in the form of an active compliance control law. An AMS-based learning approach is used to accommodate the compliance behaviour of the system and utilized as an additional feedforward loop in the control system. The applicability of the control strategy is verified by simulation