Network-Centric First Responder Architecture with Swarming Robots Entity

This paper proposes a new network centric architecture that can be used by first responders to effectively respond to crisis situations. The powerful network-centric concept originally developed for and mainly used in the military environment, can be effectively used for civilian security and emergency response missions. This paper also proposes the use of a swarm of intelligent robots as a part of the network-centric architecture to aid the first responders. The swarm of robots works in tandem with the first responders and provides them with the necessary information on a real time basis. The proposed network centric architecture with a swarming robot entity is explained in detail using C4ISR framework. The proposed architecture if implemented successfully will result in solving crisis situations, may it be natural calamity or terrorist attacks, more efficiently and effectively

Domain adaptive control architecture for advanced manufacturing systems

The main objective of this study is to design, develop, and investigate domain adaptive control architecture for advanced manufacturing systems. The proposed control architecture is a swarm-based, multi-agent system that exhibits adaptive and emergent behavior that has been inspired from social insects, such as wasps --Abstract, page iii

Swarm Based Systems of Systems Behavior Simulation for Network-centric Enterprise

Lot of research is underway to deploy Network Centric Systems in the near future. Network Centric Systems (NCS) are achieved by networking the enterprise entities to create shared situational awareness derived from common operating information that is relevant, timely, and accurate. Swarm Intelligence describes the way that complex behaviors can evolve from large number of autonomous agents following simple rules. Swarming is seemingly amorphous, but it is deliberately structured, coordinated, strategic way to solve a common goal by simple interactions between the agents and the environment. This paper aims to present a new swarm based network centric framework for a flexible manufacturing system. Successful implementation of the proposed framework will meet the increasing demands for robustness to disturbances, adaptability and flexibility to sudden changes on a shop floor of manufacturing enterprise

Fuzzy-neuro System for Bridge Health Monitoring

Many civil and mechanical systems are in continuous use despite aging and associated risk for damage accumulation. Hence, the ability to monitor the structural health of these systems on a real-time basis is becoming very important. This paper describes a practical real-time structural health monitoring system using smart engineering tools and its application to the structural health monitoring of a steel bridge located in Missouri. Vibration data collected from this bridge was processed and fed to the fuzzy logic decision system. The fuzzy logic decision system makes use of fuzzy clustering to determine the possible presence of damage in the bridge. A neural network prediction system which makes use of back propagation algorithm predicts the amount of actual damage in the members which were predicted damaged by the fuzzy logic

Real-time Routing in Flexible Flow Shops: a Self-adaptive Swarm-Based Control Model

This paper presents a self-adaptive, swarm-based control model for real-time part routing in a flexible flow-shop environment. The proposed control model is a multi-agent system that exhibits adaptive behaviour, which has been inspired from the natural system of the wasp colony. The production problem, which has been previously studied in the literature by several researchers, involves assigning trucks to paint booths in real-time in a flexible flow-shop environment with the objective of throughput maximization and minimization of number of paint flushes accrued by the production system, assuming no a priori knowledge of the colour sequence or colour distribution of trucks is available. The proposed control model is benchmarked with the results of previous studies reported in the literature in solving the same production problem. The proposed control model uses self-adapting threshold parameters to facilitate the production flow in real time. A simulation-based software environment is designed and developed to investigate its performance. The simulation results show that the proposed control model is more robust to environmental changes, and it outperforms the previously reported studies on the basis of throughput, number of setups, and cycle time performance measures

Real-time Routing Selection for Automated Guided Vehicles in a Flexible Manufacturing System

Purpose - the purpose of this paper is to present the development of an architecture for real-time routing of automated guided vehicles (AGV) in a random flexible manufacturing system (FMS). Design/methodology/approach - AGV routing problem is modeled using an evolutionary algorithm-based intelligent path planning model, which handles vehicle assignments to material handling requests and makes routing decisions with the objective of maximizing the system throughput. The architecture is implemented on a 3-layer software environment in order to evaluate the effectiveness of the proposed model. Findings - the proposed architecture, along with the evolutionary algorithm-based routing model, is implemented in a simulated FMS environment using hypothetical production data. In order to benchmark the performance of the path planning algorithm, the same FMS model is run by traditional dispatching rules. The analysis shows that the proposed routing model outperforms the traditional dispatching rules for real-time routing of AGVs in many cases. Research limitations/implications - Future work includes expanding the scope of the current work by developing and implementing other routing models and benchmarking them against the proposed model on different performance measures. Originality/value - the implementation of evolutionary algorithms in real-time routing of AGVs is unique. In addition, due to its modularity, the proposed 3-layer architecture can allow effective and efficient integration of different real-time routing algorithms; therefore it can be used as a benchmarking platform

Intelligent Path Planning and Scheduling for Automated Guided Vehicles Using Evolutionary Algorithm

This paper presents a new intelligent path planning and scheduling method for automated guided vehicles in a flexible namufacturing system. The main objective is to minimize the total distance traveled by a loaded vehicle. The path planning and scheduling algorithm is coded in MATLAB using evolutionary algoruthm. The proposed algorithm has considerable advantages over a number of previous efforts and produces reasonably good reults very quickly and hence can be used for real time planning and scheduling of automated guided vehicles. The evolutionary algorithm presented is also flexible, in the sense that it can be used for any number of vehicles and any number of machines in the shop floor with slight modification