11,220 research outputs found
CAN Fieldbus Communication in the CSP-based CT Library
In closed-loop control systems several realworld entities are simultaneously communicated to through a multitude of spatially distributed sensors and actuators. This intrinsic parallelism and complexity motivates implementing control software in the form of concurrent processes deployed on distributed hardware architectures. A CSP based occam-like architecture seems to be the most convenient for such a purpose. Many, often conflicting, requirements make design and implementation of distributed real-time control systems an extremely difficult task. The scope of this paper is limited to achieving safe and real-time communication over a CAN fieldbus for an\ud
existing CSP-based framework
A Minimal Architecture for General Cognition
A minimalistic cognitive architecture called MANIC is presented. The MANIC
architecture requires only three function approximating models, and one state
machine. Even with so few major components, it is theoretically sufficient to
achieve functional equivalence with all other cognitive architectures, and can
be practically trained. Instead of seeking to transfer architectural
inspiration from biology into artificial intelligence, MANIC seeks to minimize
novelty and follow the most well-established constructs that have evolved
within various sub-fields of data science. From this perspective, MANIC offers
an alternate approach to a long-standing objective of artificial intelligence.
This paper provides a theoretical analysis of the MANIC architecture.Comment: 8 pages, 8 figures, conference, Proceedings of the 2015 International
Joint Conference on Neural Network
CSP channels for CAN-bus connected embedded control systems
Closed loop control system typically contains multitude of sensors and actuators operated simultaneously. So they are parallel and distributed in its essence. But when mapping this parallelism to software, lot of obstacles concerning multithreading communication and synchronization issues arise. To overcome this problem, the CT kernel/library based on CSP algebra has been developed. This project (TES.5410) is about developing communication extension to the CT library to make it applicable in distributed systems. Since the library is tailored for control systems, properties and requirements of control systems are taken into special consideration. Applicability of existing middleware solutions is examined. A comparison of applicable fieldbus protocols is done in order to determine most suitable ones and CAN fieldbus is chosen to be first fieldbus used. Brief overview of CSP and existing CSP based libraries is given. Middleware architecture is proposed along with few novel ideas
Dynamic Multilevel Graph Visualization
We adapt multilevel, force-directed graph layout techniques to visualizing
dynamic graphs in which vertices and edges are added and removed in an online
fashion (i.e., unpredictably). We maintain multiple levels of coarseness using
a dynamic, randomized coarsening algorithm. To ensure the vertices follow
smooth trajectories, we employ dynamics simulation techniques, treating the
vertices as point particles. We simulate fine and coarse levels of the graph
simultaneously, coupling the dynamics of adjacent levels. Projection from
coarser to finer levels is adaptive, with the projection determined by an
affine transformation that evolves alongside the graph layouts. The result is a
dynamic graph visualizer that quickly and smoothly adapts to changes in a
graph.Comment: 21 page
A new model for solution of complex distributed constrained problems
In this paper we describe an original computational model for solving
different types of Distributed Constraint Satisfaction Problems (DCSP). The
proposed model is called Controller-Agents for Constraints Solving (CACS). This
model is intended to be used which is an emerged field from the integration
between two paradigms of different nature: Multi-Agent Systems (MAS) and the
Constraint Satisfaction Problem paradigm (CSP) where all constraints are
treated in central manner as a black-box. This model allows grouping
constraints to form a subset that will be treated together as a local problem
inside the controller. Using this model allows also handling non-binary
constraints easily and directly so that no translating of constraints into
binary ones is needed. This paper presents the implementation outlines of a
prototype of DCSP solver, its usage methodology and overview of the CACS
application for timetabling problems
A Study On Distributed Model Predictive Consensus
We investigate convergence properties of a proposed distributed model
predictive control (DMPC) scheme, where agents negotiate to compute an optimal
consensus point using an incremental subgradient method based on primal
decomposition as described in Johansson et al. [2006, 2007]. The objective of
the distributed control strategy is to agree upon and achieve an optimal common
output value for a group of agents in the presence of constraints on the agent
dynamics using local predictive controllers. Stability analysis using a
receding horizon implementation of the distributed optimal consensus scheme is
performed. Conditions are given under which convergence can be obtained even if
the negotiations do not reach full consensus.Comment: 20 pages, 4 figures, longer version of paper presented at 17th IFAC
World Congres
Design and implementation of the land surface model NaturalEnvironment within the generic framework OpenDanubia for integrative, distributed environmental modelling
The project GLOWA-Danube (http://www.glowa-danube.de) aimed at
investigating the manifold consequences of Global Change on regional water
resources in the Upper Danube Basin. In order to achieve this task, an
interdisciplinary, university-based network of experts developed the integrative
Decision Support System OpenDanubia (OD). The common base for implementing
and coupling the various scientific model components is a generic framework,
which provides the coordination of the coupled models that run in parallel
exchanging iteratively data via their interfaces. The OD framework takes care of
technical aspects, such as ordered data exchange between sub-models, data
aggregation, data output, model parallelization and data distribution over the
network, which means that model developers do not have to be concerned about
complexities evolving from coupling their models.
Within this framework the sub-model NaturalEnvironment, representing a land
surface model, was developed and implemented. The object-oriented design of this
sub-model facilitates a plain, logical representation of the actual physical processes
simulated by the sub-model. Physical processes to be modelled are organized in
naturally ordered, exchangeable lists that are executed on each spatial
computation unit for each modelling time step, depending on their land cover. The
type of land cover to be simulated on each freely defined spatial unit is
distinguished by one of the three types aquatic, terrestrial and glacier. Additionally,
the type terrestrial is influenced by dynamic land use changes which can be
triggered e.g. by the socio-economic OD sub-model Farming.
This paper presents the basic design of the open source (GPL'ed) OD framework
and highlights the implementation of the sub-model NaturalEnvironment within this
framework, as well as its interactions with other components included in OD
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
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