Force Control Command Synthesis for Constrained Hybrid Dynamic Systems with Friction

A new hybrid dynamic controller synthesis methodology for the successful convergence of force-controlled assembly tasks with friction is presented. Hybrid dynamic modeling has been shown to be a very effective strategy to incorporate both the continuous-time and discrete-event natures of an assembly task. Previously, hybrid dynamic controllers have used velocity control. This paper develops a hybrid dynamic controller that uses force control, the accepted paradigm for constrained motion systems such as assembly tasks. Frictional forces present a significant problem for force control and cannot be neglected in the development of a controller. Constraints on the control command are developed for each type of single-contact transition, and then further constraints are developed to ensure that superposition can be used for multicontact situations. Experimental results are presented for the new controller synthesis method performing an assembly task with a 0.8 mm tolerance and requiring four degrees of freedom for completion. These experiments demonstrate the effectiveness of the combination of hybrid dynamic control and force control for assembly tasks, successfully completing the assembly with positioning errors of up to 50 mm and orientation errors of up to 10 degrees

Robust Sensing for Force-Controlled Assembly

This workshop introduces a number of statistical tools that can be used to increase the reliability and robustness of force-controlled assembly tasks. The emphasis is on efficient, model-based sensor processing methods, and not on the force control aspects proper. The statistical tools are applied at different levels of the control hierarchy: set-point control, contact state monitoring, and action planning. All techniques are illustrated by experiments. The interpretation and critical assessment of the performance of the presented stochastic methods prevails over their detailed theoretical developments (for which the interested reader should consult the references)

Skill Acquisition from Human Demonstration Using a Hidden Markov Model

A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controller. This structure is naturally encoded by a hidden Markov model (HMM). The HMM parameters are obtained by training on sensory data from human demonstrations of the skill. Currently, assembly tasks have to be performed by human operators or by robots using expensive fixtures. Our approach transfers the assembly skill from an expert human operator to the robot, thus making it possible for a robot to perform assembly tasks without the use of expensive fixtures. 1 Introduction Manipulation tasks such as assembly are easily performed by human operators. However, these tasks are still difficult for robots and require the use of precise and expensive fixtures. Furthermore, human operators are ab..