The Discrete Event Control of Robotic Assembly Tasks

1995 ◽  
Vol 117 (3) ◽  
pp. 384-393 ◽  
Author(s):  
B. J. McCarragher ◽  
H. Asada

A new approach to process modeling, task synthesis, and motion control for robotic assembly is presented. Assembly is modeled as a discrete event dynamic system using Petri nets, incorporating both discrete and continuous aspects of the process. The discrete event modelling facilitates a new, task-level approach to the control of robotic assembly. To accomplish a desired trajectory a discrete event controller is developed. The controller issues velocity commands that direct the system toward the next desired contact state, while maintaining currently desired contacts and avoiding unwanted transitions. Experimental results are given for a dual peg-in-the-hole example. The experimental results not only demonstrate highly successful insertion along the optimal trajectory, but also demonstrate the ability to detect, recognize and recover from errors and unwanted situations.

2021 ◽  
Vol 101 (3) ◽  
Author(s):  
Korbinian Nottensteiner ◽  
Arne Sachtler ◽  
Alin Albu-Schäffer

AbstractRobotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.


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