6 research outputs found
Deterministic boundary recongnition and topology extraction for large sensor networks
We present a new framework for the crucial challenge of
self-organization of a large sensor network. The basic scenario can
be described as follows: Given a large swarm of immobile sensor
nodes that have been scattered in a polygonal region, such as a
street network. Nodes have no knowledge of size or shape of the
environment or the position of other nodes. Moreover, they have no
way of measuring coordinates, geometric distances to other nodes, or
their direction. Their only way of interacting with other nodes is
to send or to receive messages from any node that is within
communication range. The objective is to develop algorithms and
protocols that allow self-organization of the swarm into large-scale
structures that reflect the structure of the street network, setting
the stage for global routing, tracking and guiding algorithms.
Our algorithms work in two stages: boundary recognition and topology
extraction. All steps are strictly deterministic, yield fast
distributed algorithms, and make no assumption on the distribution
of nodes in the environment, other than sufficient density
Characterising delamination in composite materials : a combined genetic algorithm - finite element approach
A novel delamination identification technique based on a low-population genetic algorithm for the quantitative characterisation of a single delamination in composite laminated panels is developed, and validated experimentally The damage identification method is formulated as an inverse problem through which system parameters are identified. The input of the inverse problem, the central geometric moments (CGM), is calculated from the surface out-of-plane displacements measurements of a delaminated panel obtained from Digital Speckle Pattern Interferometry (DSPI). The output parameters, the planar location, size and depth of the flaw, are the solution to the inverse problem to characterise an idealised elliptical flaw. The inverse problem is then reduced to an optimisation problem where the objective function is defined as the L2 norm of the difference between the CGM obtained from a finite element (FE) model with a trial delamination and the moments computed from the DSPI measurements. The optimum crack parameters are found by minimising the objective function through the use of a low-population real-coded genetic algorithm (LARGA). DSPI measurements of ten delaminated T700/LTM-45EL carbon/epoxy laminate panels with embedded delaminations are used to validate the methodology presented in this thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach
In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations
Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach
In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations
Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera
This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808
Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera
This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808