This paper introduces a novel approach to ontology-based robot plan transfer by leveraging functorial data migrations, a structured mapping method derived from category theory. Functors provide structured maps between planning domain ontologies which enables the transfer of task plans without the need for replanning. Unlike methods tailored to specific plans, our framework applies universally within the source domain once a structured map is defined. We demonstrate this approach by transferring a task plan from the canonical Blocksworld domain to one compatible with the AI2-THOR Kitchen environment. Additionally, we discuss practical limitations, propose benchmarks for evaluating symbolic plan transfer methods, and outline future directions for scaling this approach.

RobotCat uses a goal-oriented, declarative approach for robot programming that leverages mathematical representations found in category theory as a way to formally model modularity and behavioral knowledge. We define hierarchical interfaces that require only local modifications when new software or work cell models are introduced, thus making robot programming more interoperable.Note to Practitioners—RoboCat can be put into practice and be beneficial to different stakeholders: 1) a student (or small, individual manufacturer) is in charge of planning the production of a new product. Instead of tedious programming, she sketches out process diagrams using conceptual templates for actions (e.g., “move,” “carry,” “locate”) that can produce vastly more resilient production plans than would normally be expected from someone without many years of experience; 2) a robot platform manufacturer has a new manipulator that they would like to provide to the existing customers. Usually, such an upgrade requires a complex retooling of the fabrication lines to accommodate the new functionality. Further complicating matters, the new manipulator contains third-party hardware and software, requiring a full re-architecting of the integrated system. Rather than a wholesale swap of the old system for the new, the third-party application programming interfaces (APIs) can be systematically translated to fit snuggly within the existing modular system with seamless interoperability; and 3) a company is designing the production process associated with their new product launch and they wish to spread their supply chain across several manufacturing vendors. Rather than writing individual process plans customized for each manufacturer, they compose a high-level plan in a functional language. This plan is compiled into implementations specific to each manufacturer’s systems, without the need for human intervention.

The recent growth of agent-based software systems was achieved without the development of a reference architecture. From a software engineering standpoint, a reference architecture is necessary to compare, evaluate, and integrate past, current, and future agent-based software systems. The agent systems reference architecture (ASRA) advances the agent-based system development process by providing a set of key interaction patterns for functional areas that exist between the layers and protocols of agent-based systems. Furthermore, the ASRA identifies the points for interoperability between agent-based systems and increases the level of discussion when referring to agent-based systems. This paper presents methodology, grounded in software forensics, to develop the ASRA and provides an overview of the resulting architectural representation. The methodology uses an approach based on software engineering techniques adapted to study agent frameworks-the libraries and tools for building agent systems. The resulting ASRA can serve as an abstract representation of the components necessary for facilitating comparison, integration, and interoperation of software systems composed of agents.

There are hundreds of distinct 3D, CAD and engineering file formats. As engineering design and analysis has become increasingly digital, the proliferation of file formats has created many problems for data preservation, data exchange, and interoperability. In some situations, physical file objects exist on legacy media and must be identified and interpreted for reuse. In other cases, file objects may have varying representational expressiveness. We introduce the problem of automated file recognition and classification in emerging digital engineering environments, where all design, manufacturing and production activities are “born digital.” The result is that massive quantities and varieties of data objects are created during the product lifecycle. This paper presents an approach to automated identification of engineering file formats. This work operates independent of any modeling tools and can identify families of related file objects as well as variations in versions. This problem is challenging as it cannot assume any a priori knowledge about the nature of the physical file object. Applications for these methods include support for a number of emerging applications in areas such as forensic analysis, data translation, as well as digital curation and long-term data management.

The Next-Generation Transportation program coordinates the evolution and transformation of the current air-traffic management (ATM) system for the National Airspace System (NAS). Currently the NAS has a limited capacity and cannot handle the increasing future air traffic demands. However, before newly proposed ATM concepts are deployed they must be rigorously evaluated under realistic conditions. This paper presents AGENTFLY, an emerging NAS-wide highfidelity multi-agent ATM simulator with precise emulation of the human controller operation workload model and human-system interaction. The simulator is validated using a flight scenario developed by the U.S. Federal Aviation Administration that is based on real data. We present preliminary results focusing on the accuracy of the simulated controllers within AGENTFLY.

Biomimetic robots are a new and challenging frontier for robotic systems. Designs inspired by nature are creating new approaches to problems such as to planetary surface exploration, minimally invasive surgery, or inspection of piping and cabling. However, these new biomimetic robotic systems are a challenge to design, simulate, and control. This paper presents a case-study in the design and physics-based simulation of a unique snake-inspired robot. The Drexel Snake Robot is a novel hybrid capable of both undulatory and rectilinear motion. In order to design gaits, test control algorithms and perform path planning for this robot, we develop a system-level physics-based simulation that captures engineering phenomena across many disciplines: mechanical, electrical, software, electronics, and the robot's external environment. As the snake robot moves, there is considerable slippage between its feet and the ground. Consequently, contact and friction forces play a significant role in dictating the path followed by the robot for a given set of joint motions. A closed-form equation (or set of equations) describing the robot's motion in this environment cannot be derived in a simple manner. Consequently, the material presented here are based the comparison of experimental and simulation results. While developing the simulation model, we detail the process of extracting necessary physical, kinematic and dynamics properties directly from the robot's computer-aided design. This process is not straightforward and the paper documents the issues and lessons learned that will be of use to others wishing to create full virtual models for their systems. Finally, we show how to calibrate the simulation model for fidelity and accuracy with several examples showing that it can be used to test gait and mobility patterns and identify nonobvious secondary phenomena to emerge from the design.

This paper presents the development of a set of ontologies for use in messaging systems within military and emergency first responder command and control applications. These ontologies are used to annotate and subscribe to messages within a content delivery and service discovery middleware connecting sensors, services, and agents on the network. Ontologies serve an important role in this middleware in providing for flexible addressing and querying that increases the decoupling between senders and receivers afforded by the middleware. This is particularly important in the target applications, which feature heterogenous components and software versions spanning development organizations and deployment time. Discussed here are application uses, development methodology, evaluation criteria, and other notes on these ontologies and their creation. The goal of this paper is to provide useful input to others engaged in similar development efforts.

The ability to interpret and reason about shapes is a peculiarly human capability that has proven difficult to reproduce algorithmically. So despite the fact that geometric modeling technology has made significant advances in the representation, display and modification of shapes, there have only been incremental advances in geometric reasoning. For example, although today's CAD systems can confidently identify isolated cylindrical holes, they struggle with more ambiguous tasks such as the identification of partial symmetries or similarities in arbitrary geometries. Even well defined problems such as 2D shape nesting or 3D packing generally resist elegant solution and rely instead on brute force explorations of a subset of the many possible solutions.

Digital preservation is the mitigation of the deleterious effects of technology obsolescence, media degradation, and fading human memory. For engineering, design, manufacturing, and physics-based simulation data this requires formats that are semantically accessible for 30-to-50 year lifespans. One of the fundamental challenges is the development of digital geometry-centric engineering representations that are self describing and assured to be interpretable over the long lifespans required by archival applications.

Agent-based systems have been the object of intense research over the past decade. While great theoretical progress has been made, the software frameworks for creating agent-based systems offer considerable variability in their capabilities and functionality. This paper introduces a reference model for agent-based systems. The purpose of a reference model is to provide a common conceptual basis for comparing systems and driving the development of software architectures and other standards. The Foundation for Intelligent Physical Agents and other groups have advanced a number of agent standards, yet, to date, no comprehensive reference model has been presented for software systems composed of agents. This paper provides an overview of a reference model for agent-based systems. The agent systems reference model is the result of a multiyear effort studying software systems built with agents and software frameworks for implementing these systems. As part of this study, the team applied software reverse engineering techniques to perform static and dynamic analysis of operational agent-based systems. This analysis enabled identification of key common concepts across over one dozen different agent frameworks. To demonstrate its applicability, the reference model is then used to analyze a number of complete agent-based software systems. It is the belief of the authors that the reference model will be an essential prerequisite for future transition, deployment, and integration of agent-based systems.

The design of robotic systems involves contributions from several areas of science and engineering. Electrical, mechanical and software components must be integrated to form the final system. Increasingly, simulation tools are being introduced into the design flow as a means to verify the performance of particular subsystems. In order to accurately simulate the complete robotic system we propose a framework that allows designers to describe the robotic system as an interconnection of mechanical, electrical, and software components, with well defined mechanisms for communicating with each other. Through this, we form a multi-disciplinary model that captures both the dynamics of the individual subsystems, and the dynamics resulting from the interconnection of the above subsystems. As a case-study, we will apply the framework to a biologically inspired robotic snake.

The complexity and diversity of the vascular system makes it difficult to perform quantitative blood vessel modeling and analysis. Emerging applications in biomedical modeling, bio-mimetic manufacturing and tissue engineering necessitate the development of new approaches to computational modeling of vascular structures. In this paper, we address the computational representation of realistic vascular objects to enable deformable blood vessels. First, we construct a swept volume based vascular model from a physiological vascular model. Second, we construct the NURBS surface based on deformable circles, i.e., the primitive of swept volume, as the boundary of the swept volumes. The approach of NURBS surface construction can also be extended to reconstruct medical image based vasclar models. In the end, potential applications of the mathematical representation of vascular structure including blood flow simulation in scaffold are discussed. As a computational representation, our vascular model enables multiscale visualization of a realistic vascular system.

While benchmark datasets have been proposed for testing computer vision and 3D shape retrieval algorithms, no such datasets have yet been put forward to assess the relevance of these techniques for engineering problems. This paper presents several distinctive benchmark datasets for evaluating techniques for automated classification and retrieval of CAD objects. These datasets include (1) a dataset of CAD primitives (such as those common in constructive solid geometry modeling); (2) two datasets consisting of classes generated by minor topological variation; (3) two datasets of industrial CAD models classified based on object function and manufacturing process, respectively; (4) and a dataset of LEGO© models from the Mindstorms© robotics kits. Each model in the datasets is available in three formats - ACIS SAT, ISO STEP, and as a VRML mesh (some models are available under several different fidelity settings). These are all available through the National Design Repository.Using these datasets, we present comprehensive empirical results for nińe (9) different shape and solid model matching and retrieval techniques. These experiments show, as expected, that the quality of precision-recall performance can significantly vary on different datasets. These experiments reveal that for certain object classes and classifications, such as those based on manufacturing processes, all existing techniques perform poorly. This study reveals the strengths and weaknesses of existing research in these areas, introduces open challenge problems, and provides meaningful datasets and metrics against which the success of current and future work can be measured.

This paper used surface curvatures and support vector machines for the identification of manufacturing processes in a database of mesh based CAD artifacts. The target is to classify prismatic machined and cast-then-machined parts into their respective classification to assist the manufacturing cost estimation. Current research on shape matching techniques has used shape functions to match the gross shapes of mesh models. However, they do not adequately discriminate artifacts manufactured by different processes. Our approach shows how different kinds of surfaces can distinguish different manufacturing processes. Statistics on surface curvatures are used to construct shape descriptors; then supervised machine learning classifier support vector machines are applied to separate the two classifications.

Creating surfaces with intricate small-scale features (microgeometry) and detail is an important task in geometric modeling and computer graphics. We present a model processing method capable of producing a wide variety of complex surface features based on displacement mapping and stochastic geometry. The latter is a branch of mathematics that analyzes and characterizes the statistical properties of spatial structures. The technique has been incorporated into an interactive modeling environment that supports the design of stochastic microgeometries. Additionally a tool has been developed that provides random exploration of the technique's entire parameter space by generating sample microgeometry over a broad range of values. We demonstrate the effectiveness of our technique by creating diverse, complex surface structures for a variety of geometric models, e.g. arrowheads, candy bars, busts, planets and coral.

This paper presents an approach, experiments and a practical application to balancing service availability for a multi-agent system deployed on a wireless mobile ad hoc network. Finding the optimal number of services required to achieve the desired effect is a difficult and problem-specific task. We propose an approach inspired by the quorum sensing behavior of the leptothorax albipennis ants. Similar to a real ant colony, the solution exhibits properties of emergent stability, decentralized control, and resilience to possible disturbances. The experiments confirm the validity of our technique in wireless ad hoc network environments.

Properly educating computer scientists involves teaching effective means to properly engineer a system. An important part of such engineering work is ensuring that the computing system is both useful and usable. While many systems out there today are difficult to use, performing usability engineering on a system during its development has been shown to be an effective way to make a system more usable. The problem is fitting practical experience into the curriculum. This paper discusses a case example of how a team of undergraduate students learned to take a software system during its developing stages and perform effective usability engineering following the "thinking out loud" methodology.

Solid models are the critical data elements in modern computer-aided design (CAD) environments, describing the shape and form of manufactured artifacts. Their growing ubiquity has created new problems in how to effectively manage the many models that are now stored in the digital libraries of large design and manufacturing enterprises. Existing techniques from the engineering literature and from industrial practice, such as group technology, rely on human-supervised encoding and classification, while techniques from the multimedia database and computer graphics/vision communities often ignore the manufacturing attributes most significant in the classification of models. This paper presents our approach to the manufacturing similarly assessment of solid models of mechanical parts based on machining features. Our technical approach is three-fold: (1) perform machining feature extraction to map the solid model to a set of STEP AP 224 machining features; (2) construct a model dependency graph from the set of machining features; and (3) find the nearest neighbors to the query graph using an iterative improvement search across a database of other models. We also present empirical experiments to validate our approach using our testbed, the National Design Repository (). The contribution of this research is the first fully automated technique for machining feature-based comparisons of mechanical artifacts. We believe that this work can lead to radical changes in the way in which design data is managed in modern engineering enterprises.

The field of solid modeling has developed a variety of techniques for unambiguous representations of three-dimensional objects. Feature recognition is a sub-discipline of solid modeling that focuses on the design and implementation of algorithms for detecting manufacturing information from solid models produced by computer-aided design (CAD) systems. Examples of this manufacturing information include features such as holes, slots, pockets and other shapes that can be created on modern computer numerically controlled machining systems. Automated feature recognition has been an active research area in solid modeling for many years and is considered to be a critical component for integration of CAD and computer-aided manufacturing. The paper gives an overview of the state-of-the-art in feature recognition research. Rather than giving an exhaustive survey, we focus on the three of the major algorithmic approaches for feature recognition: graph-based algorithms, volumetric decomposition techniques, and hint-based geometric reasoning. For each approach, we present a detailed description of the algorithms being employed along with some assessments of the technology. We conclude by outlining important open research and development issues.