27,424 research outputs found

    Representing Resources in Petri Net Models: Hardwiring or Soft-coding?

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    ©2011 IEEE. Reprinted, with permission, from : Reggie Davidrajuh; Representing Resources in Petri Net Models : Hardwiring or Soft-coding?, 2011 IEEE International Conference on Service Operations, Logistics, and Informatics (SOLI), 2011; Beijing, China. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Stavanger's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs‐[email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper presents an interesting design problem in developing a new tool for discrete-event dynamic systems (DEDS). A new tool known as GPenSIM was developed for modeling and simulation of DEDS; GPenSIM is based on Petri Nets. The design issue this paper talks about is whether to represent resources in DEDS hardwired as a part of the Petri net structure (which is the widespread practice) or to soft code as common variables in the program code. This paper shows that soft coding resources give benefits such as simpler and skinny models

    On the Specification and Verification of Communication Protocols

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    The purpose of this paper is to consider the most complicated problem related to computer network design, and especially to the so-called "gateways": the definition and estimation of the logical correctness of protocols. While the simple terminal connection of a computer to a computer system necessitates only the emulation of the chosen terminal, the very complicated interconnection of several computer networks requires the definition and implementation of a whole hierarchy of protocols. Naturally, all the protocols of each level must be rigorously specified and carefully verified before being implemented into soft-, firm-, or hardware. In order to achieve this goal, a technique based on a top-down approach, involving stepwise refinement and verification of the protocol actions in various situations, is proposed in this paper. This technique requires the formalism of a special kind of Petri net: the Petri net with enabling predicates

    Multiform Adaptive Robot Skill Learning from Humans

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    Object manipulation is a basic element in everyday human lives. Robotic manipulation has progressed from maneuvering single-rigid-body objects with firm grasping to maneuvering soft objects and handling contact-rich actions. Meanwhile, technologies such as robot learning from demonstration have enabled humans to intuitively train robots. This paper discusses a new level of robotic learning-based manipulation. In contrast to the single form of learning from demonstration, we propose a multiform learning approach that integrates additional forms of skill acquisition, including adaptive learning from definition and evaluation. Moreover, going beyond state-of-the-art technologies of handling purely rigid or soft objects in a pseudo-static manner, our work allows robots to learn to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control. Such capability of robotic manipulation offers a variety of new possibilities in human-robot interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC), Tysons Corner, VA, October 11-1

    Task planning with uncertainty for robotic systems

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    In a practical robotic system, it is important to represent and plan sequences of operations and to be able to choose an efficient sequence from them for a specific task. During the generation and execution of task plans, different kinds of uncertainty may occur and erroneous states need to be handled to ensure the efficiency and reliability of the system. An approach to task representation, planning, and error recovery for robotic systems is demonstrated. Our approach to task planning is based on an AND/OR net representation, which is then mapped to a Petri net representation of all feasible geometric states and associated feasibility criteria for net transitions. Task decomposition of robotic assembly plans based on this representation is performed on the Petri net for robotic assembly tasks, and the inheritance of properties of liveness, safeness, and reversibility at all levels of decomposition are explored. This approach provides a framework for robust execution of tasks through the properties of traceability and viability. Uncertainty in robotic systems are modeled by local fuzzy variables, fuzzy marking variables, and global fuzzy variables which are incorporated in fuzzy Petri nets. Analysis of properties and reasoning about uncertainty are investigated using fuzzy reasoning structures built into the net. Two applications of fuzzy Petri nets, robot task sequence planning and sensor-based error recovery, are explored. In the first application, the search space for feasible and complete task sequences with correct precedence relationships is reduced via the use of global fuzzy variables in reasoning about subgoals. In the second application, sensory verification operations are modeled by mutually exclusive transitions to reason about local and global fuzzy variables on-line and automatically select a retry or an alternative error recovery sequence when errors occur. Task sequencing and task execution with error recovery capability for one and multiple soft components in robotic systems are investigated

    Robot Composite Learning and the Nunchaku Flipping Challenge

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    Advanced motor skills are essential for robots to physically coexist with humans. Much research on robot dynamics and control has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this paper, we present a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation. The method tackles advanced motor skills that require dynamic time-critical maneuver, complex contact control, and handling partly soft partly rigid objects. We also introduce the "nunchaku flipping challenge", an extreme test that puts hard requirements to all these three aspects. Continued from our previous presentations, this paper introduces the latest update of the composite learning scheme and the physical success of the nunchaku flipping challenge

    An NMF solution for the Petri Nets to State Charts case study at the TTC 2013

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    Software systems are getting more and more complex. Model-driven engineering (MDE) offers ways to handle such increased complexity by lifting development to a higher level of abstraction. A key part in MDE are transformations that transform any given model into another. These transformations are used to generate all kinds of software artifacts from models. However, there is little consensus about the transformation tools. Thus, the Transformation Tool Contest (TTC) 2013 aims to compare different transformation engines. This is achieved through three different cases that have to be tackled. One of these cases is the Petri Net to State Chart case. A solution has to transform a Petri Net to a State Chart and has to derive a hierarchical structure within the State Chart. This paper presents the solution for this case using NMF Transformations as transformation engine.Comment: In Proceedings TTC 2013, arXiv:1311.7536. arXiv admin note: substantial text overlap with arXiv:1312.034
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