Distributed control of industrial robot systems using Petri net based multitask processing
In this paper, the methods of the modeling of the discrete event robotic manufacturing systems using Petri nets are considered, and a methodology of decomposition and coordination is presented for hierarchical and distributed control, where the cooperation of each controller is implemented so that the behavior of the overall system is the same as the centralized control system and the task specification is completely satisfied. First, a task specification is defined as a Petri net model at the conceptual level, and then transformed to the detailed Petri net representation of the subtasks. Finally, the overall Petri net is decomposed and the constituent subnets are assigned to local Petri net based controllers. The controllers are coordinated so that the decomposed transitions fire at the same time by the coordinator. System coordination algorithm through communication between the coordinator and the controllers, is presented. By the proposed method, modeling, simulation and control of large and complex manufacturing systems can be performed consistently using Petri nets.
Towards a BSN-based gesture interface for intelligent home applications
This paper presents an attempt towards the development of a hand gesture-based interface for intelligent home applications using Body Sensor Networks (BSNs). The system being developed consists of four main components namely BSN sensor gloves as the input devices, a software prototype for facilitating mappings of sensory signals onto different commands, a model for automatic gesture recognition and a relay-board for demonstrating device control output. Our current results include the first version of software prototype and real-time recognition of simple gesture commands from signals acquired using the BSN-based flex-sensor glove.
Application of hierarchical production planning in a multiproduct, multimachine environment
The paper proposes a model designed to deal with a hierarchical production planning/scheduling problem in a multiproduct, multimachine environment with sequence dependent set-ups. Problems of this type have been known to be difficult to solve owing to their heavy computational burden. The model proposed here incorporates the concept of expected set-up costs and the expertise of manager input at the aggregate planning level in order significantly to reduce the computational burden. When tested with the actual plant data, the model outperformed other models reported in the literature.