Increasing complexity of Industrial automated systems requires high level discrete event control implementation in programmable logic controllers (PLCs). To date most PLC applications for industrial discrete event control systems (DECS) are basically heuristic, mostly supported by partial system analysis and limited performance evaluation. Consequently the application of formal methods in PLC programming became extremely essential for industrial automation. During the past few decades Petri Nets (PN) have evolved to a very promising formal tool to establish an integrated solution for modeling, analysis, simulation and control of industrial automated systems. This paper presents a methodology for implementation of high level DECS PN models in PLCs using IEC 1131-3 standard programming languages. The produced PLC code is completely dual to its original PN model capturing completely its dynamic properties and can be fully restored directly from its PLC code. The flexibility of the PN models is reflected in the DECS's PLC code being simply and accurately updated following the changes conducted in the DECS's model.
Index Terms-Petri Net (PN), Discrete Event Control System (DECS), Programmable Logic Controller (PLC).
The involvement of Robots and automated machines in different industries has increased drastically in recent years. Part of this revolution is accomplishing tasks previously performed by humans with advanced robots, which would replace the entire human workforce in the future. In some industries the workers are required to complete different operations in hazardous or difficult environments. Operations like these could be replaced with the use of tele-operated systems that have the capability of grasping objects in their surroundings, thus abandoning the need for the physical presence of the human operator at the area while still allowing control. In this research our goal is to create an assisting system that would improve the grasping of a human operator using a tele-operated robotic gripper and arm, while advising the operator but not forcing a solution. For a given set of objects we computed the optimal grasp to be achieved by the gripper, based on two grasp quality measures of our choosing (namely power grasp and precision grasp). We then tested the performance of different human subjects who tried to grasp the different objects with the tele-operated system, while comparing their success to unassisted and assisted grasping. Our goal is to create an assisting algorithm that would compute optimal grasps and might be integrated into a complete, state-of-the-art tele-operated system.
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