Emilio Miguelanez

An IEEE standard Ontology for Robotics and Automation

This article discusses a newly formed IEEE-RAS working group entitled Ontologies for Robotics and Automation (ORA). The goal of this working group is to develop a standard ontology and associated methodology for knowledge representation and reasoning in robotics and automation, together with the representation of concepts in an initial set of application domains. The standard provides a unified way of representing knowledge and provides a common set of terms and definitions, allowing for unambiguous knowledge transfer among any group of humans, robots, and other artificial systems. In addition to describing the goal and structure of the group, this article gives some examples of how the ontology, once developed, can be used by applications such as industrial kitting.

Semantic Knowledge-Based Framework to Improve the Situation Awareness of Autonomous Underwater Vehicles

This paper proposes a semantic world model framework for hierarchical distributed representation of knowledge in autonomous underwater systems. This framework aims to provide a more capable and holistic system, involving semantic interoperability among all involved information sources. This will enhance interoperability, independence of operation, and situation awareness of the embedded service-oriented agents for autonomous platforms. The results obtained specifically affect the mission flexibility, robustness, and autonomy. The presented framework makes use of the idea that heterogeneous real-world data of very different type must be processed by (and run through) several different layers, to be finally available in a suited format and at the right place to be accessible by high-level decision-making agents. In this sense, the presented approach shows how to abstract away from the raw real-world data step by step by means of semantic technologies. The paper concludes by demonstrating the benefits of the framework in a real scenario. A hardware fault is simulated in a REMUS 100 AUV while performing a mission. This triggers a knowledge exchange between the status monitoring agent and the adaptive mission planner embedded agent. By using the proposed framework, both services can interchange information while remaining domain independent during their interaction with the platform. The results of this paper are readily applicable to land and air robotics.

Semantic knowledge-based representation for improving situation awareness in service oriented agents of autonomous underwater vehicles

This paper proposes a semantic world model framework for hierarchical distributed representation of knowledge in autonomous underwater systems. This framework aims to provide a more capable and holistic system, involving semantic interoperability, among all involved information sources. This will enhance interoperability, independence of operation, and situation awareness of the embedded service-oriented agents for autonomous platforms. The results obtained specificially impact om mission flexibility robustness and autonomy. The presented framework makes use of the idea that heterogeneous real-world data of very different types must be proceed by (and run through) several different layers to be finally available in a suited format and at the right place to be acccesible by high-level decision making agents. In this sense, the presented approach shows how to abstract away from the raw real-world data step by step by means of semantic technologies. The paper concludes by demonstrating the benefits of the framework in a real scenario. A hardware fault is simulated in a REMUS 100 AUV while performing a mission. This triggers a knowledge exchange between the incipient fault diagnosis agent and the adaptive mission planner embedded agent. By using the proposed framework, both services can interchange information while remaining domain independent during their interaction with the platform. The results of this paper are readily applicable to land and air robotics.

Adaptive mission plan diagnosis and repair for fault recovery in autonomous underwater vehicles

This paper proposes a novel approach for autonomous mission diagnosis and repair for maintaining operability of unmanned underwater vehicles. It combines the benefits of knowledge-based ontology representation, autonomous partial ordering plan repair and robust mission execution. The approach uses the potential of ontology reasoning in order to orient the planning algorithms adapting the mission plan of the vehicle. It can handle uncertainty and action scheduling in order to maximize mission efficiency and minimise mission failures due to external or unexpected factors. Its performance is presented in a set of simulated scenarios. The paper concludes by showing the results of a trial demonstration. Observations of different environmental and internal parameters are simulated in a REMUS 100 AUV while performing a mission. These trigger a knowledge exchange between the diagnosis monitor agent and the adaptive mission planner embedded agent. Based on the observed data and the original knowledge, the experiment shows how the adaptive planner system is able to identify the gaps in the mission and adapt the platforms mission plan accordingly.

Fault tolerant adaptive mission planning with semantic knowledge representation for autonomous underwater vehicles

This paper proposes a novel approach for autonomous mission plan recovery for maintaining operability of unmanned underwater vehicles. It combines the benefits of knowledge-based ontology representation, autonomous partial ordering plan repair and robust mission execution. The approach uses the potential of ontology reasoning in order to orient the planning algorithms adapting the mission plan of the vehicle. It can handle uncertainty and action scheduling in order to maximize mission efficiency and minimise mission failures due to external unexpected factors. Its performance is presented in a set of simulated scenarios for different concepts of operations for the underwater domain. The paper concludes by showing the results of a trial demonstration carried out on a real underwater platform. The results of this paper are readily applicable to land and air robotics.

Swarm intelligence in automated electrical wafer sort classification

The semiconductor manufacturing domain is by no doubt a rich and challenging environment for the application of machine learning. Some of the demanding characteristic of semiconductor data include high dimensionality, mixtures of categorical and numerical data, non-randomly Gaussian data, non-Gaussian and multi-modal distributions, highly non-linear complex relationships, noise and outliers in both x and y dimensions, temporal dependencies, etc. These challenges are becoming particularly crucial as the quantity of available data is growing dramatically. This paper addresses the problem of automatic wafer manufacturing process error detection based on electrical wafer sort (EWS) parametric tests. A wafer-to-wafer analysis is presented that automatically detect possible errors in the manufacturing process that causes systematic damage to the product as it passes through some step in the process. Possible causes are equipment mishandling, operator error, material issues such as contamination, etc. These manufacturing errors are reflected on the physical and electrical properties of the wafers, which are measured by the parametric tests in the EWS process. The core of this research is a novel classifier system based on the benefits arising from the interaction between evolutionary algorithms and artificial neural networks. Experimental results demonstrates that this system is able to detect defective wafers with an accuracy of 82%. Prior to the EWS classification, the proposed system is evaluated with three classification benchmark problems: Iris dataset, Australian credit card problem, and the Pumas diabetes dataset. The obtained results are compared with classifiers outcomes available in the literature.

Automating the Analysis of Wafer Data Using Adaptive Resonance Theory Networks

In semiconductor manufacturing, finding needles in haystacks is easy compared with finding sub micron defects in modern ICs like complex microprocessors. The problem is likely to grow much worse as the relative complexity of chips (number of transistors and total wiring length) increases as the size of the smallest defects that can cause failures decreases. The use of unsupervised learning is a promising strategy towards the development of fully automated classification tools. This research intends to develop an automatic defect classification system for electrical test analysis of semiconductor wafer using an adaptive resonance theory network as a classifier. As a primary input source to the network, the system employs ebinmaps obtained from the test stage of the manufacturing process. To accomplish this task, a filtering algorithm is also implemented able to discard those wafermaps without pattern. This paper reports satisfactory results showing that the proposed system can recognised defect spatial patterns with a 82% correct e-binmap classification rate.

An Ontology-Based Approach to Fault Tolerant Mission Execution for Autonomous Platforms

Autonomous underwater vehicles (AUVs) have become a standard tool for data gathering and intervention in security, offshore and marine science applications. In these environments, mission effectiveness directly depends on vehicle’s operability. Operability underlies the vehicle’s ultimate availability. Two main vehicle characteristics can improve operability: reliability depends on the internal hardware components of the vehicle and survivability, a concept that is closely related with vehicle failures due to external factors or damages.