Abstract
Force guided assembly is a control scheme to guide a workpiece based on a stored map from forces to a correction of motion. Based on the geometry of the workpiece and its kinematic behavior in interacting with the environment, the functional map relating the correction of motion to force measurements is generated and stored as a control law. Central to the design of force guided control is how to synthesize this functional map. Although these explicit force-guided controls are a useful concept, particularly for the monitoring of assembly processes, there are inherent difficulties in applying it to real world problems.
In real assembly lines, pipe insertion task, for instance, has been performed only by human workers. Skilled workers insert pipes by perturbing the pipes in order to avoid jamming as well as to determine which way to correct the motion. According to them, the skilled workers monitor obstructing forces in response to the applied perturbation, and modify their motion accordingly. The proposed perturbation/correlation method was motivated by this human perception and action : perturbing the pipe, observing the reaction to the perturbation and correcting the trajectory.
In this paper, we propose a novel technique for acquiring effective force information despite sensor noise and friction. Instead of simply receiving force signals from the process, we give perturbation to the robot and measure the reaction forces to the perturbation. By taking the correlation between the perturbation signal and the reaction forces, reliable and useful information for guiding the robot would be extracted. It is expected that this perturbed force measurement provides much richer force information than that of stationary measurement. The perturbation/Correlation method presented in this paper is not only effective for reducing friction, but also effective for obtaining useful information for guiding the robot towards a desired direction. Preliminary experimental results with one directional perturbation are shown in this paper. Extensive mathematical analysis shows the potential application to assemblies in higher dimension.
AI based combined scheduling and motion planning in flexible robotic assembly lines
