Teleoperation of ABB industrial robots

Purpose - The purpose of this paper is to analyse teleoperation of an ABB industrial robot with an ABB IRC5 controller. A method to improve motion smoothness and decrease latency using the existing ABB IRC5 robot controller without access to any low-level interface is proposed. Design/methodology/ approach - The proposed control algorithm includes a high-level proportional-integral-derivative controller (PID) controller used to dynamically generate reference velocities for different travel ranges of the tool centre point (TCP) of the robot. Communication with the ABB IRC5 controller was performed utilising the ABB PC software development kit. The multitasking feature of the IRC5 controller was used to enhance the communication frequency between the controller and the remote application. Trajectory tracking experiments of a pre-defined three-dimensional trajectory were carried out and the benefits of the proposed algorithm were demonstrated. The robot was intentionally installed on a wobbly table and its vibrations were recorded using a six-degrees-of-freedom force/torque sensor fitted to the tool mounting interface of the robot. The robot vibrations were used as a measure of the smoothness of the tracking movements. Findings - A communication rate of up to 250 Hz between the computer and the controller was established using C#.Net. Experimental results demonstrating the robot TCP, tracking errors and robot vibrations for different control approaches were provided and analysed. It was demonstrated that the proposed approach results in the smoothest motion with tracking errors of < 0.2 mm. Research limitations/implications - The proposed approach may be employed to produce smooth motion for a remotely operated ABB industrial robot with the existing ABB IRC5 controller. However, to achieve high-bandwidth path following, the inherent latency of the controller must be overcome, for example by utilising a low-level interface. It is particularly useful for applications including a large number of short manipulation segments, which is typical in teleoperation applications. Social implications - Using the proposed technique, off-the-shelf industrial robots can be used for research and industrial applications where remote control is required. Originality/value - Although low-level control interface for industrial robots seems to be the ideal long-term solution for teleoperation applications, the proposed remote control technique allows out-of-the-box ABB industrial robots with IRC5 controllers to achieve high efficiency and manipulation smoothness without requirements of any low-level programming interface. © Copyright - 2014 Emerald Group Publishing Limited. All rights reserved.

Improvements in teleoperation of industrial robots without low-level access

 This paper proposes a method to improve motion smoothness and decrease latency using existing ABB IRC5 robot controllers without access to any low level interface. The proposed control algorithm includes a high-level PID controller used to dynamically generate reference velocities for different travel ranges of the tool centre point (TCP) of the robot. Communication with the ABB IRC5 controller was performed utilising the ABB PC software development kit (SDK). The multitasking feature of the IRC5 controller was used in order to enhance the communication frequency between the controller and the remote application. Trajectory tracking experiments of a predefined 3D trajectory were carried out and the benefits of the proposed algorithm was demonstrated. The robot was intentionally installed on a wobbly table and its vibrations were recorded using a six degrees of freedom (DOF) force/torque sensor fitted to the tool mounting interface of the robot. The robot vibrations were used as a measure of the smoothness of the tracking movements. Experimental results demonstrating the robot tool centre point (TCP), tracking errors, and robot vibrations for different control approaches were provided and analysed. It was demonstrated that the proposed approach results in the smoothest motion with less than 0.2 mm tracking errors.

Internet traffic classification using constrained clustering

Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches. In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new patterns keep emerging. Although previous studies have applied some classic clustering algorithms such as K-Means and EM for the task, the quality of resultant traffic clusters was far from satisfactory. In order to improve the accuracy of traffic clustering, we propose a constrained clustering scheme that makes decisions with consideration of some background information in addition to the observed traffic statistics. Specifically, we make use of equivalence set constraints indicating that particular sets of flows are using the same application layer protocols, which can be efficiently inferred from packet headers according to the background knowledge of TCP/IP networking. We model the observed data and constraints using Gaussian mixture density and adapt an approximate algorithm for the maximum likelihood estimation of model parameters. Moreover, we study the effects of unsupervised feature discretization on traffic clustering by using a fundamental binning method. A number of real-world Internet traffic traces have been used in our evaluation, and the results show that the proposed approach not only improves the quality of traffic clusters in terms of overall accuracy and per-class metrics, but also speeds up the convergence.

Analysis of backward congestion notification with delay for enhanced Ethernet networks

At present, companies and standards organizations are enhancing Ethernet as the unified switch fabric for all of the TCP/IP traffic, the storage traffic and the high performance computing traffic in data centers. Backward congestion notification (BCN) is the basic mechanism for the end-to-end congestion management enhancement of Ethernet. To fulfill the special requirements of the unified switch fabric, i.e., losslessness and low transmission delay, BCN should hold the buffer occupancy around a target point tightly. Thus, the stability of the control loop and the buffer size are critical to BCN. Currently, the impacts of delay on the performance of BCN are unidentified. When the speed of Ethernet increases to 40 Gbps or 100 Gbps in the near future, the number of on-the-fly packets becomes the same order with the buffer size of switch. Accordingly, the impacts of delay will become significant. In this paper, we analyze BCN, paying special attention on the delay. We model the BCN system with a set of segmented delayed differential equations, and then deduce sufficient condition for the uniformly asymptotic stability of BCN. Subsequently, the bounds of buffer occupancy are estimated, which provides direct guidelines on setting buffer size. Finally, numerical analysis and experiments on the NetFPGA platform verify our theoretical analysis.