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2018-02-02
Mirkhanzadeh, B., Shao, C., Shakeri, A., Sato, T., Razo-Razo, M., Tacca, M., Fumagalli, A., Yamanaka, N..  2017.  A two-layer network Orchestrator offering trustworthy connectivity to a ROS-industrial application. 2017 19th International Conference on Transparent Optical Networks (ICTON). :1–4.

This paper describes an experiment carried out to demonstrate robustness and trustworthiness of an orchestrated two-layer network test-bed (PROnet). A Robotic Operating System Industrial (ROS-I) distributed application makes use of end-to-end flow services offered by PROnet. The PROnet Orchestrator is used to provision reliable end-to-end Ethernet flows to support the ROS-I application required data exchange. For maximum reliability, the Orchestrator provisions network resource redundancy at both layers, i.e., Ethernet and optical. Experimental results show that the robotic application is not interrupted by a fiber outage.

2017-10-19
Ko, Wilson K.H., Wu, Yan, Tee, Keng Peng.  2016.  LAP: A Human-in-the-loop Adaptation Approach for Industrial Robots. Proceedings of the Fourth International Conference on Human Agent Interaction. :313–319.

In the last few years, a shift from mass production to mass customisation is observed in the industry. Easily reprogrammable robots that can perform a wide variety of tasks are desired to keep up with the trend of mass customisation while saving costs and development time. Learning by Demonstration (LfD) is an easy way to program the robots in an intuitive manner and provides a solution to this problem. In this work, we discuss and evaluate LAP, a three-stage LfD method that conforms to the criteria for the high-mix-low-volume (HMLV) industrial settings. The algorithm learns a trajectory in the task space after which small segments can be adapted on-the-fly by using a human-in-the-loop approach. The human operator acts as a high-level adaptation, correction and evaluation mechanism to guide the robot. This way, no sensors or complex feedback algorithms are needed to improve robot behaviour, so errors and inaccuracies induced by these subsystems are avoided. After the system performs at a satisfactory level after the adaptation, the operator will be removed from the loop. The robot will then proceed in a feed-forward fashion to optimise for speed. We demonstrate this method by simulating an industrial painting application. A KUKA LBR iiwa is taught how to draw an eight figure which is reshaped by the operator during adaptation.