Multi-sensor measurement system for robotic drilling

In this paper we present a multi-sensor measurement system for robotic drilling. The system enables a robot to measure its 6D pose with respect to the work piece and to establish a reference coordinate system for drilling. The robot approaches the drill point and performs an orthogonal alignment with the work piece. Although the measurement systems are readily capable of achieving high position accuracy and low deviation to perpendicularity, experiments show that inaccuracies in the test scenario and slippage on the work piece when exerting clamping force considerably impact the results. With the current robotic drilling system an average position deviation of 0.334 mm and an average deviation to perpendicularity of 0.29° are achieved.     

A performance evaluation methodology for robotic machine tools used in large volume manufacturing

The manufacture of large components in various industries can create health, safety and economic challenges as a result of machining operations. A potential solution is the replacement of conventional large machine tools with low-cost and portable robotic machine tools, although these lack accuracy and precision in comparison. Effort is therefore required to develop robotic machining technology to a state where it can be implemented in high tolerance applications using a variety of materials. A barrier to implementation is that there is not a standardised procedure available to robustly assess current robotic machining performance, which makes it challenging to assess the impact of technological developments. This paper develops a methodology to determine robotic machining performance based upon reviews of standards available that currently specify such guidelines for robotics and machine tool technologies used independently. It is found that useful elements from each theme can be combined and applied to robotic machine tool performance evaluation. These are presented here with the aim of forming a conceptual foundation on which the technology can be developed for large volume manufacturing.     

A three-dimensional measurement system for robot applications

This paper presents a 3D noncontacting sensor system designed to measure the position and orientation of a robot end effector. This measurement system includes two parts: a tridimensional object including four spheres placed along the axes of a tetrahedron and a set of three orthogonally pointed cameras. The purpose is to design a measurement system characterized by easy relationships in order to satisfy real-time constraints. The system has been used in two experiments: first, to calibrate a parallel robot and validate the geometrical control performance, then as an exteroceptive sensor in an assembly task. The system computes position and orientation of the tetrahedron in 100 ms time. The position and orientation accuracy are, respectively, 0.6 mm and 0.2 deg in a workspace, being a cube with 0.3 m sides.     

Motion coordination for industrial robotic systems with redundant degrees of freedom

This work investigates how to distribute in an optimum fashion the desired movement of the end-effector of an industrial robot with respect to the workpiece, when there are redundant degrees of freedom, such as a positioning table. The desired motion is given as a series of acceleration functions in respective time intervals. The constraints of the optimisation are the available acceleration limit of axes, such as the table axes, the upper bounds to velocity and displacement of each axis and the avoidance of singular point areas of the robot, as defined by its manufacturer. The optimisation criterion is minimum total work for the motion. A genetic algorithm was used to solve the problem. The fitness function of the genetic algorithm calls a kinematics and dynamics simulation model of the robotic installation constructed in Matlab™, in order to compute the work consumed and to check possible violation of constraints. Examples of straight line and circular movement are given to prove the concept. Results are encouraging, yet demand on computing power is high.     

Image-based recognition framework for robotic weed control systems

In this paper, we introduce a novel and efficient image-based weed recognition system for the weed control problem of Broad-leaved Dock (Rumex obtusifolius L.). Our proposed weed recognition system is developed using a framework, that allows the examination of the affects for various image resolutions in detection and recognition accuracy. Moreover, it includes state-of-the-art object/image categorization processes such as feature detection and extraction, codebook learning, feature encoding, image representation and classification. The efficiency of those processes have been improved and optimized by introducing methodologies, techniques and system parameters specially tailored for the goal of weed recognition. Through an exhaustive optimization process, which is presented as our experimental evaluation, we conclude to a weed recognition system that uses an image input resolution of 200 ×150, SURF features over dense feature extraction, an optimized Gaussian Mixture Model based codebook combined with Fisher encoding, using a two level image representation. The resulting image representation vectors are classified using a linear classifier. This system is experimentally shown to yield state-of-the-art recognition accuracy of 89.09% in the examined dataset. Our proposed system is also experimentally shown to comply with the specifications of the examined applications since it provides low false-positive results of 4.38%. As a result, the proposed framework can be efficiently used in weed control robots for precision farming applications.     

A general framework for statistical performance comparison of evolutionary computation algorithms

This paper proposes a statistical methodology for comparing the performance of evolutionary computation algorithms. A twofold sampling scheme for collecting performance data is introduced, and these data are analyzed using bootstrap-based multiple hypothesis testing procedures. The proposed method is sufficiently flexible to allow the researcher to choose how performance is measured, does not rely upon distributional assumptions, and can be extended to analyze many other randomized numeric optimization routines. As a result, this approach offers a convenient, flexible, and reliable technique for comparing algorithms in a wide variety of applications.     

A framework for performance measurement in the e-business environment

The advent of networked economy calls for new understanding of business, and it is evidenced by the visible trend of traditional businesses either migrating to e-business or expanding to embrace electronic commerce. Constant change in the environment means continually evolving strategies, new products, new processes and new technologies to adopt. E-business metrics are needed to measure performance with the firm’s strategic focus in mind, and they must go beyond the Web metrics that are discussed in the usual electronic commerce context. The basic objective of this paper is to present a framework for developing performance measurement metrics in the e-business environment. The proposed framework, designed by incorporating the balanced scorecard methodology with existing taxonomies of e-business models and the theories behind them, is intended to enable firms to develop new metrics that are needed to implement e-business strategies and tactics.     

A framework for performance measurement in the e-business environment

The advent of networked economy calls for new understanding of business, and it is evidenced by the visible trend of traditional businesses either migrating to e-business or expanding to embrace electronic commerce. Constant change in the environment means continually evolving strategies, new products, new processes and new technologies to adopt. E-business metrics are needed to measure performance with the firm’s strategic focus in mind, and they must go beyond the Web metrics that are discussed in the usual electronic commerce context. The basic objective of this paper is to present a framework for developing performance measurement metrics in the e-business environment. The proposed framework, designed by incorporating the balanced scorecard methodology with existing taxonomies of e-business models and the theories behind them, is intended to enable firms to develop new metrics that are needed to implement e-business strategies and tactics.     

Human interaction with robotic systems: performance and workload evaluations

Abstract

We first tested the effect of differing tactile informational forms (i.e. directional cues vs. static cues vs. dynamic cues) on objective performance and perceived workload in a collaborative human–robot task. A second experiment evaluated the influence of task load and informational message type (i.e. single words vs. grouped phrases) on that same collaborative task. In both experiments, the relationship of personal characteristics (attentional control and spatial ability) to performance and workload was also measured. In addition to objective performance and self-report of cognitive load, we evaluated different physiological responses in each experiment. Results showed a performance–workload association for directional cues, message type and task load. EEG measures however, proved generally insensitive to such task load manipulations. Where significant EEG effects were observed, right hemisphere amplitude differences predominated, although unexpectedly these latter relationships were negative. Although EEG measures were partially associated with performance, they appear to possess limited utility as measures of workload in association with tactile displays.

Practitioner Summary: As practitioners look to take advantage of innovative tactile displays in complex operational realms like human–robotic interaction, associated performance effects are mediated by cognitive workload. Despite some patterns of association, reliable reflections of operator state can be difficult to discern and employ as the number, complexity and sophistication of these respective measures themselves increase.     

A support-design framework for Cooperative Robots systems in labor-intensive manufacturing processes

Manufacturing processes and industrial systems gradually change their traditional layouts and configurations, preparing to introduce novel integrated human-robot technologies as collaborative robots and exoskeletons. Whether mass customization of lot size and the production mix discourages the adoption of capital-intensive automation, collaborative robots become affordable and effective and a hotspot of the debate on manufacturing systems. This paper provides a novel support-design framework for the cooperative robot system in labor-intensive manufacturing processes to aid layout and task scheduling design. Through an iterative closed-loop methodology, this framework explores the impact of a cooperative robot in a labour-intensive manufacturing system like the production facility of a food service company. The framework leads the designer through the re-layout of the end-of-line, the economic and technical feasibility analyses, using simulation to estimate payback and ergonomics benefits for workers. Within the proposed layout, we state that adopting a cooperative cobot for the end-of-line is affordable and ergonomically convenient without representing a safety threat for workers. The testbed confirms the framework as an enabling tool for human-robot technologies integration in current manufacturing systems under budget and workers-driven constraints.     

Approximate performance modeling and decision making for manufacturing systems: A queueing network optimization framework

In this paper we discuss queueing network methodology as a framework to address issues that arise in the design and planning of discrete manufacturing systems. Our review focuses on three aspects: modeling of manufacturing facilities, performance evaluation and optimization with queueing networks. We describe both open and closed network models and present several examples from the literature illustrating applications of the methodology. We also provide a brief outline of outstanding research issues. The paper is directed towards the practitioner with operations research background and the operations management researcher with interest in this topic.     

A framework for developing measurement systems and its industrial evaluation

As in every engineering discipline, metrics play an important role in software development, with the difference that almost all software projects need the customization of metrics used. In other engineering disciplines, the notion of a measurement system (i.e. a tool used to collect, calculate, and report quantitative data) is well known and defined, whereas it is not as widely used in software engineering. In this paper we present a framework for developing custom measurement systems and its industrial evaluation in a software development unit within Ericsson. The results include the framework for designing measurement systems and its evaluation in real life projects at the company. The results show that with the help of ISO/IEC standards, measurement systems can be effectively used in software industry and that the presented framework improves the way of working with metrics. This paper contributes with the presentation of how automation of metrics collection and processing can be successfully introduced into a large organization and shows the benefits of it: increased efficiency of metrics collection, increased adoption of metrics in the organization, independence from individuals and standardized nomenclature for metrics in the organization.     

A multimodal teaching advisor for sensor-enhanced robotic systems in manufacturing

A multimodal teaching advisor (MTA) has been developed for sensor-enhanced robotic systems used in manufacturing. The MTA utilizes the work-site operator's know-how and robotic-systems information, including that from sensors, in a complementary manner. This system integrates information from various sources to carry out robotic tasks and presents this information through user interfaces that are easily understood by the operator. That is, the system integrates task specifications including tolerances, system constraints, sensory information, and operator- and robot-related information. The synthesized information is then presented in realtime to the operator at the work-site through a mobile multimodal interface. Experimental results show that this system can significantly improve total robotic system performance by ensuring a high-quality teaching task.     

The NEXUS open system for integrating robotic software

In this paper a framework for constructing flexible, robust and efficient software applications for robots is described. The basic concepts needed to integrate complex, multidisciplinary robot software architectures are identified, and the methods to achieve them are taken from different areas of research (programming languages, network communication systems, real-time systems, etc.). The result is an open software system called NEXUS which includes the basic characteristics needed for the integration of very different software modules, minimizing the effort of integration and maximizing the reusability, efficiency and robustness of the resulting software applications. This software has proven to be a basis for more sophisticated tools that help in reducing the cost of modifications to and the complexity of multidisciplinary projects, allowing highly structured and reusable designs to be implemented. Although it has been currently implemented for mobile robots, it is a sufficiently generic framework suitable for use in other control systems.     

PHOEBE: an automation framework for the effective usage of diagnosis tools in the performance testing of clustered systems

The identification of performance issues and the diagnosis of their root causes are time‐consuming and complex tasks, especially in clustered environments. To simplify these tasks, researchers have been developing tools with built‐in expertise for practitioners. However, various limitations exist in these tools that prevent their efficient usage in the performance testing of clusters (e.g. the need of manually analysing huge volumes of distributed results). In a previous work, we introduced a policy‐based adaptive framework (PHOEBE) that automates the usage of diagnosis tools in the performance testing of clustered systems, in order to improve a tester's productivity, by decreasing the effort and expertise needed to effectively use such tools. This paper extends that work by broadening the set of policies available in PHOEBE, as well as by performing a comprehensive assessment of PHOEBE in terms of its benefits, costs and generality (with respect to the used diagnosis tool). The performed evaluation involved a set of experiments in assessing the different trade‐offs commonly experienced by a tester when using a performance diagnosis tool, as well as the time savings that PHOEBE can bring to the performance testing and analysis processes. Our results have shown that PHOEBE can drastically reduce the effort required by a tester to do performance testing and analysis in a cluster. PHOEBE also exhibited consistent behaviour (i.e. similar time‐savings and resource utilisations), when applied to a set of commonly used diagnosis tools, demonstrating its generality. Finally, PHOEBE proved to be capable of simplifying the configuration of a diagnosis tool. This was achieved by addressing the identified trade‐offs without the need for manual intervention from the tester. Copyright © 2017 John Wiley & Sons, Ltd.     

A fault tolerance framework for cooperative robotic manipulators

The problem of fault tolerance in cooperative manipulators rigidly connected to an undeformable load is addressed in this paper. Four categories of faults are considered: free-swinging joint faults (FSJFs), locked joint faults (LJFs), incorrectly measured joint position faults (JPFs), and incorrectly measured joint velocity faults (JVFs). Free-swinging and locked joint faults are detected via artificial neural networks (ANNs). Incorrectly measured joint position and velocity faults are detected by considering the kinematic constraints of the cooperative system. When a fault is detected, the control system is reconfigured according to the nature of the isolated fault and the task is resumed to the largest extent possible. The fault tolerance framework is applied to an actual system composed of two cooperative robotic manipulators. The results presented demonstrate the feasibility and performance of the methodology.     

A framework for implementing robotic process automation projects

Robotic process automation is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes rapidly. In contrast to other process automation technologies, robotic process automation is lightweight and only accesses the presentation layer of IT systems to mimic human behavior. Due to the novelty of robotic process automation and the varying approaches when implementing the technology, there are reports that up to 50% of robotic process automation projects fail. To tackle this issue, we use a design science research approach to develop a framework for the implementation of robotic process automation projects. We analyzed 35 reports on real-life projects to derive a preliminary sequential model. Then, we performed multiple expert interviews and workshops to validate and refine our model. The result is a framework with variable stages that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments as well as for small and medium-sized companies. It is structured by the three phases of initialization, implementation, and scaling. They comprise eleven stages relevant during a project and as a continuous cycle spanning individual projects. Together they structure how to manage knowledge and support processes for the execution of robotic process automation implementation projects.

     

A probabilistic framework for real-time performance assessment of inferential sensors

A definition for the reliability of inferential sensor predictions is provided. A data-driven Bayesian framework for real-time performance assessment of inferential sensors is proposed. The main focus is on characterizing the effect of operating space on the reliability of inferential sensor predictions. A holistic, quantitative measure of the reliability of the inferential sensor predictions is introduced. A methodology is provided to define objective prior probabilities over plausible classes of reliability based on the total misclassification cost. The real-time performance assessment of multi-model inferential sensors is also discussed. The application of the method does not depend on the identification techniques employed for model development. Furthermore, on-line implementation of the method is computationally efficient. The effectiveness of the method is demonstrated through simulation and industrial case studies.     

Wave-variable framework for networked robotic systems with time delays and packet losses

This paper investigates the problem of networked control system for nonlinear robotic manipulators under time delays and packet loss by using passivity technique. With the utilisation of wave variables and a passive remote controller, the networked robotic system is demonstrated to be stable with guaranteed position regulation. For the input/output signals of robotic systems, a discretisation block is exploited to convert continuous-time signals to discrete-time signals, and vice versa. Subsequently, we propose a packet management, called wave-variable modulation, to cope with the proposed networked robotic system under time delays and packet losses. Numerical examples and experimental results are presented to demonstrate the performance of the proposed wave-variable-based networked robotic systems.