An implementing framework for holonic manufacturing control with multiple robot-vision stations
The paper describes a holonic control architecture and implementing issues for agile job shop assembly with networked intelligent robots, based on the dynamic simulation of material processing and transportation. The holarchy was defined considering the PROSA reference architecture relative to which in-line vision-based quality control was added by help of feature-based descriptions of the material flow. Two solutions for production planning are proposed: a knowledge-based algorithm using production rules, and an OO resolved scheduling rate planner (RSRP) based on variable-timing simulation. Failure- and recovery-management are developed as generic scenarios embedding the CNP mechanism into production self-rescheduling. Aggregate Order Holon execution is realized by OPC-based PLC software integration and event-driven product transportation. The holonic control of multiple networked robot-vision stations also features tolerance to station computer- (IBM PC-type), station controller- (robot controller), quality control- (machine vision) and communication- (LAN) failure. Fault tolerance and high availability at shop-floor level are provided due to the multiple physical communication capabilities of the robot controllers, to their multiple-axis multitasking operating capability, and to hardware redundancy of single points of failure (SPOF). Implementing solutions and experiments are reported for a 6-station robot-vision assembly cell with twin-track closed-loop pallet transportation system and product-racking RD/WR devices. Future developments will consider manufacturing integration at enterprise level.
holonic manufacturing robotics semi-heterarchical control real-time vision applied ai