Neuromorphic vision based control for the precise positioning of robotic drilling systems

The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for robotic machining applications to enable faster and more reliable operation, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece’s pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling end-effector and drills the target holes on the workpiece using a combined position-based and image-based visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with maximum positional errors of less than 0.2 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution.     

Web mining for event-based commonsense knowledge using lexico-syntactic pattern matching and semantic role labeling

A sophisticated commonsense knowledgebase is essential for many intelligent system applications. This paper presents a methodology for automatically retrieving event-based commonsense knowledge from the web. The approach is based on matching the text in web search results to designed lexico-syntactic patterns. We apply a semantic role labeling technique to parse the extracted sentences so as to identify the essential knowledge associated with the event(s) described in each sentence. Particularly, we propose a semantic role substitution strategy to prune knowledge items that have a high probability of erroneously parsed semantic roles. The experimental results in a case study for retrieving the knowledge is “capable of” shows that the accuracy of the retrieved commonsense knowledge is around 98%.     

State-based versus event-based specifications for information systems: a comparison of B and eb 3

This paper compares two formal methods, B and eb³, for specifying information systems. These two methods are chosen as examples of the state-based paradigm and the event-based paradigm, respectively. The paper considers four viewpoints: functional behavior expression, validation, verification, and evolution. Issues in expressing event ordering constraints, data integrity constraints, and modularity are thereby considered. A simple case study is used to illustrate the comparison, namely, a library management system. Two equivalent specifications are presented using each method. The paper concludes that B and eb³ are complementary. The former is better at expressing complex ordering and static data integrity constraints, whereas the latter provides a simpler, modular, explicit representation of dynamic constraints that are closer to the user’s point of view, while providing loosely coupled definitions of data attributes. The generality of these results from the state-based paradigm and the event-based paradigm perspective are discussed.     

Event-based consensus of multi-agent systems with general linear models

In this paper, the event-based consensus problem of general linear multi-agent systems is considered. Two sufficient conditions with or without continuous communication between neighboring agents are presented to guarantee the consensus. The advantage of the event-based strategy is the significant decrease of the number of controller updates for cooperative tasks of multi-agent systems involving embedded microprocessors with limited on-board resources. The controller updates of each agent are driven by properly defined events, which depend on the measurement error, the states of its neighboring agents and an arbitrarily small threshold. It is shown that the controller updates for each agent only trigger at its own event time instants. A simulation example is presented to illustrate the theoretical results.     

An event-triggered approach to state estimation with multiple point- and set-valued measurements

In this work, we consider state estimation based on the information from multiple sensors that provide their measurement updates according to separate event-triggering conditions. An optimal sensor fusion problem based on the hybrid measurement information (namely, point- and set-valued measurements) is formulated and explored. We show that under a commonly-accepted Gaussian assumption, the optimal estimator depends on the conditional mean and covariance of the measurement innovations, which applies to general event-triggering schemes. For the case that each channel of the sensors has its own event-triggering condition, closed-form representations are derived for the optimal estimate and the corresponding error covariance matrix, and it is proved that the exploration of the set-valued information provided by the event-triggering sets guarantees the improvement of estimation performance. The effectiveness of the proposed event-based estimator is demonstrated by extensive Monte Carlo simulation experiments for different categories of systems and comparative simulation with the classical Kalman filter.     

A time-resolved Monte Carlo smoke model for use at optical and infrared frequencies

This paper describes a statistical smoke model which can be of help to validate applications and technologies which imply propagation of electromagnetic waves through smoke-filled atmospheres. The approach followed is a time-resolved Monte Carlo simulation for use in quantitative as well as qualitative analysis of different types of smokes. The model has been designed to operate at optical and infrared frequencies for short distance propagation, although it can be extended to other frequencies and longer propagation distances. The simulation environment has been treated from both an optical and a geometrical point of view, and a flexible and convenient simulation framework has been presented. The model can be fed with data from types of smoke with arbitrary distributions of particle sizes and optical behaviors. Also, different emitter, receiver and obstacle geometries can be defined. An extended set of simulation setups shows the preliminary potential of the model.     

Event-based intelligent control of saturated chemical plant using an endomorphic neural network model

An event-based control system with an endomorphic neural network model is designed and realized to control a saturated non-linear plant. The scheme employed in this system is based on an event-based control paradigm previously proposed to control monotonic plants. However, this scheme is different from the previous one in that it can be used to control plants with saturation property. This new scheme may be viewed as a combined method of a time-based diagnosis mechanism in an event-based control system and a state-based control mechanism in a neural network control system. A chemical plant having strong non-linearity and complicated dynamics is controlled using this realized event-based control system. This paper discusses the structure of an event-based controller, the neural network modelling methodology, some related problems, and the simulation results.     

Integral-based event triggering controller design for stochastic LTI systems via convex optimisation

The presence of measurement noise in the event-based systems can lower system efficiency both in terms of data exchange rate and performance. In this paper, an integral-based event triggering control system is proposed for LTI systems with stochastic measurement noise. We show that the new mechanism is robust against noise and effectively reduces the flow of communication between plant and controller, and also improves output performance. Using a Lyapunov approach, stability in the mean square sense is proved. A simulated example illustrates the properties of our approach.     

Automating performance-related impact analysis through event based traceability

Current impact analysis techniques tend to focus on assessing the impact of change upon the system's functionality, whilst a consideration of performance related requirements is often deferred until after implementation. This tendency can lead to costly and time-consuming mistakes that frustrate customers and require frantic last-minute efforts to fix. This paper proposes a method for supporting performance-related impact analysis in a heterogeneous software engineering environment. An event-based approach is taken to establish dynamic traceability links, capable of propagating data values and commands between requirements and performance models. Quantitative values in performance related requirements are adjusted to reflect proposed changes, and impacted models are re-executed to measure the impact of the change. The resulting outputs are then automatically compared to relevant performance requirements and a system-wide report showing the impact of the proposed change upon performance is generated.

Event-based failure prediction in distributed business processes

Traditionally, research in Business Process Management has put a strong focus on centralized and intra-organizational processes. However, today’s business processes are increasingly distributed, deviating from a centralized layout, and therefore calling for novel methodologies of detecting and responding to unforeseen events, such as errors occurring during process runtime. In this article, we demonstrate how to employ event-based failure prediction in business processes. This approach allows to make use of the best of both traditional Business Process Management Systems and event-based systems. Our approach employs machine learning techniques and considers various types of events. We evaluate our solution using two business process data sets, including one from a real-world event log, and show that we are able to detect errors and predict failures with high accuracy.