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Estymacja czasów wykonywania algorytmu sterującego w zależności od platformy sprzętowej na użytek diagnostyki obiektu mechanicznego

Kozłowska A.

Pomiary Automatyka Kontrola

2013

R. 59, nr 5

466--469

Opracowanie systemów sterowania obiektami mechanicznymi polega na znalezieniu kompromisu między szybkością działania, a wymaganą dokładnością i jest zagadnieniem o dużej złożoności obliczeniowej. W artykule przedstawiono różne implementacje algorytmu Optymalizacji Rojem Cząstek PSO (ang. Particle Swarm Optimization), który stworzono w celu uzyskania minimalnego czasu obróbki przy zachowaniu zadanej dokładności odtwarzania trajektorii ruchu. Jego działanie zostało porównane w językach: C, C++ i C# oraz na procesorze i karcie graficznej. Z przeprowadzonych badań wynika, że dla małej liczby punktów obliczenia na karcie graficznej są wolniejsze niż na procesorze.

: Finding the compromise between speed and accuracy is the most important problem in designing control systems. This is a problem of high computational complexity. The paper presents implementation of the algorithm PSO (Particle Swarm Optimization) whose action has been compared in several programming environments (C / OpenCL and C # / Cloo and in C + +) and hardware platforms (CPU and graphics card processor - GPU). PSO is able to achieve the minimum processing time and best possible mapping of a given trajectory. To compare the speed of the PSO algorithm there was made a measurement of the time of test function minimization. The paper describes three test functions commonly used to test the optimization effectiveness. The results show that for a small number of points the calculations on a graphic card are slower than those performed on the CPU. The appropriate use of available parallel computing technologies can significantly improve the characteristics of a multi-axis machine and the expenses incurred for optimization of the PSO can quickly result in important profits. It should be noted that optimization of the processing speed is most needed where the treatment is most complicated. The profit will be negligible for simple trajectories. In special cases, the optimization may extend the processing time without apparent improvement of the characteristics of trajectory mapping.

Integration of vision based image processing for multi-axis CNC machine tool safe and efficient trajectory generation and collision avoidance

Ahmad R., Tichadou S., Hascoet J.-Y.

Journal of Machine Engineering

2010

Vol. 10, No. 4

53--65

Demand of high speed production not only increases the complexity of today's CNC process, but also increases the risks and possibility of collision because of the difference between real (machining) and virtual scenes (CAD/CAM process). Idea here is to make this process more intelligent by processing image taken from the real or virtual machine scenes. Identify objects (already known in the CAD database), obtain safe and efficient trajectories that will modify the previous known trajectories from the CAM systems and will be used finally in real machining environments. This work more focuses to improve trajectory generation, collision avoidance and communication in CAD/CAM systems by image processing technique. Safe and Efficient Trajectory (SET) algorithm for point trajectory is discussed along with its extended version for object trajectory known as Rectangular Enveloped object Safe and Efficient Trajectory (RESET) algorithm that will perfectly generate safe un-functional trajectories [3] for multi-axis machine tool envelop. Meanwhile scene objects are detected and identified by image processing tool while trajectory and setup is optimized and improved accordingly in order to avoid collision. This generated trajectory can be used for setup correction before and after production or for "real times/online" production. Finally work has been validated through real and virtual machine scene images.