167,911 research outputs found
Discrete Event Command and Control for Formation Flying of Distributed Small Spacecraft Systems
A distributed, multi-vehicle concept for future space missions has been conceived as a solution to the problem of advancing space-based operations within budgetary constraints. Broadly named formation flying, this approach to designing distributed systems across multiple, spatially disbursed platforms is enabled by collectively coordinating a fleet of autonomous spacecraft to function as a unified system. Formation flying offers potential advantages of improved robustness, capability, and cost relative to complicated, single platform systems by using multiple, often small, spacecraft to perform complex multi-sensor tasks. A necessary element in the realization of formation flying systems is the development of methods and technologies that facilitate the transition from treating a distributed spacecraft system as individual elements, to viewing a formation as a coordinated system unified by common objectives. This paper describes the results of research performed to identify fundamental issues that affect the development of command and control (C2) methods applicable to coordinating distributed small spacecraft systems. A discrete event method of distributed command and control is described that is particularly well suited to small spacecraft formation flying. Utilized in many complex terrestrial systems, discrete event systems (DES) concepts facilitate coordination of distributed systems at multiple levels of resolution in an efficient manner. DES also provide a means to integrate intelligent planning and processing operations while interfacing with more traditional subsystem controllers. The basic principals and applicability of DES are described within the context of formation flying and example distributed spacecraft C2 operations are defined
Planning for the semiconductor manufacturer of the future
Texas Instruments (TI) is currently contracted by the Air Force Wright Laboratory and the Defense Advanced Research Projects Agency (DARPA) to develop the next generation flexible semiconductor wafer fabrication system called Microelectronics Manufacturing Science & Technology (MMST). Several revolutionary concepts are being pioneered on MMST, including the following: new single-wafer rapid thermal processes, in-situ sensors, cluster equipment, and advanced Computer Integrated Manufacturing (CIM) software. The objective of the project is to develop a manufacturing system capable of achieving an order of magnitude improvement in almost all aspects of wafer fabrication. TI was awarded the contract in Oct., 1988, and will complete development with a fabrication facility demonstration in April, 1993. An important part of MMST is development of the CIM environment responsible for coordinating all parts of the system. The CIM architecture being developed is based on a distributed object oriented framework made of several cooperating subsystems. The software subsystems include the following: process control for dynamic control of factory processes; modular processing system for controlling the processing equipment; generic equipment model which provides an interface between processing equipment and the rest of the factory; specification system which maintains factory documents and product specifications; simulator for modelling the factory for analysis purposes; scheduler for scheduling work on the factory floor; and the planner for planning and monitoring of orders within the factory. This paper first outlines the division of responsibility between the planner, scheduler, and simulator subsystems. It then describes the approach to incremental planning and the way in which uncertainty is modelled within the plan representation. Finally, current status and initial results are described
Requirements Engineering for Globally Distributed Teams using Scaled Agile Framework
As large organizations are striving to deliver software at a faster pace and to keep up with the latest trends, they are in a transformation stage of adopting to Scaled Agile Framework (SAFe). SAFe is a framework for implementing agile practices at enterprise level and it provides a roadmap for portfolios, programs and teams. Large organizations adopting to SAFe are facing challenges in coordinating, planning and managing requirements, as they work with globally distributed teams.
The goal of this thesis was to improve the Requirements Engineering (RE) process using Scaled Agile Framework in globally distributed teams. The main research method used in this thesis was action research, an iterative approach which combines theory and practice. The empirical study was conducted in a large project that used SAFe and had eight globally distributed teams. In order to investigate the challenges faced by globally distributed teams, analysis of the existing literature and RE process flow in SAFe was important. It served as a good input to understand which good RE practices can be applied in the empirical study.
The results of the study show that visually representing requirements as models and sharing domain and system knowledge through Community of Practice (CoP) reduced ambiguity in requirements. The good RE practice applied in SAFe, of working and improving collaboratively with the globally distributed teams helped in better coordination and managing of requirements. In addition to this, it was also essential to have SAFe training to develop clear and shared understanding of the framework and RE process.
The lessons learned from the empirical study indicate that a well-organized PI planning is the key RE practice of SAFe in providing the big picture of requirements to all members in distributed teams. In addition, Community of Practice (CoP) can be a key RE practice of SAFe in sharing knowledge such as business domain, system knowledge, skills and techniques, and experiences
Scalable Multiagent Coordination with Distributed Online Open Loop Planning
We propose distributed online open loop planning (DOOLP), a general framework
for online multiagent coordination and decision making under uncertainty. DOOLP
is based on online heuristic search in the space defined by a generative model
of the domain dynamics, which is exploited by agents to simulate and evaluate
the consequences of their potential choices.
We also propose distributed online Thompson sampling (DOTS) as an effective
instantiation of the DOOLP framework. DOTS models sequences of agent choices by
concatenating a number of multiarmed bandits for each agent and uses Thompson
sampling for dealing with action value uncertainty. The Bayesian approach
underlying Thompson sampling allows to effectively model and estimate
uncertainty about (a) own action values and (b) other agents' behavior. This
approach yields a principled and statistically sound solution to the
exploration-exploitation dilemma when exploring large search spaces with
limited resources.
We implemented DOTS in a smart factory case study with positive empirical
results. We observed effective, robust and scalable planning and coordination
capabilities even when only searching a fraction of the potential search space
Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming
Platooning connected and autonomous vehicles (CAVs) can improve traffic and
fuel efficiency. However, scalable platooning operations require junction-level
coordination, which has not been well studied. In this paper, we study the
coordination of vehicle platooning at highway junctions. We consider a setting
where CAVs randomly arrive at a highway junction according to a general renewal
process. When a CAV approaches the junction, a system operator determines
whether the CAV will merge into the platoon ahead according to the positions
and speeds of the CAV and the platoon. We formulate a Markov decision process
to minimize the discounted cumulative travel cost, i.e. fuel consumption plus
travel delay, over an infinite time horizon. We show that the optimal policy is
threshold-based: the CAV will merge with the platoon if and only if the
difference between the CAV's and the platoon's predicted times of arrival at
the junction is less than a constant threshold. We also propose two
ready-to-implement algorithms to derive the optimal policy. Comparison with the
classical value iteration algorithm implies that our approach explicitly
incorporating the characteristics of the optimal policy is significantly more
efficient in terms of computation. Importantly, we show that the optimal policy
under Poisson arrivals can be obtained by solving a system of integral
equations. We also validate our results in simulation with Real-time Strategy
(RTS) using real traffic data. The simulation results indicate that the
proposed method yields better performance compared with the conventional
method
Modelling electronic service systems using UML
This paper presents a profile for modelling systems of electronic
services using UML. Electronic services encapsulate business services,
an organisational unit focused on delivering benefit to a consumer,
to enhance communication, coordination and information management.
Our profile is based on a formal, workflow-oriented description of electronic
services that is abstracted from particular implementation technologies.
Resulting models provide the basis for a formal analysis to verify
behavioural properties of services. The models can also relate services to
management components, including workflow managers and Electronic
Service Management Systems (ESMSs), a novel concept drawn from experience
of HP Service Composer and DySCo (Dynamic Service Composer),
providing the starting point for integration and implementation
tasks. Their UML basis and platform-independent nature is consistent
with a Model-Driven Architecture (MDA) development strategy, appropriate
to the challenge of developing electronic service systems using
heterogeneous technology, and incorporating legacy systems
Coordinating Local Adaptive Strategies through a Network-Based Approach
As the impacts of climate change become increasingly destructive and pervasive, climate adaptation has received greater political and academic attention. The traditional top-down model for mitigating climate change, however, is ill-suited to implementing effective adaptation strategies. Yet, local communities most impacted by climate change seldom have the tools and resources to develop effective adaptive strategies on their own. This note argues that a bottom-up, network-based approach could be a promising paradigm towards implementing effective adaptive strategies and empowering affected communities
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