基于分布式交互仿真(DIS)概念的装甲战车训练模拟器设计与分析

B. Jayaprakash, A. Shenbagamoorthy
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引用次数: 2

摘要

本文提出了一种新的装甲战车仿真方法。战斗车辆研究与发展机构(CVRDE)计划将分布式交互仿真(DIS)概念纳入装甲战斗车辆训练模拟器。DIS是一种开放标准,用于在多台主机上进行实时平台级战争游戏。这在世界范围内使用,特别是军事组织和其他机构,如那些参与空间探索和医学的机构。在分布式交互场景中,需要一个中心节点根据接收者各自的优先级将数据转发给他们。一种称为有向最小生成树(PQPST)的优先级预测和量化协议被有效地用于预测接收方的优先级。对预测的优先级进行量化,构建有效利用网络带宽的显著分布树。CVRDE利用现有的部队训练模拟器体系结构,对PST算法和PQPST算法的性能分析进行了对比研究,并给出了结果。研究表明,PQPST算法更适合利用网络带宽,减少延迟,在训练模拟器中实现实时响应。该研究也有助于机器人应用从网络分布图中智能地选择合适的路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and analysis using Distributed Interactive Simulation (DIS) concept for Armoured Fighting Vehicle training simulators
A new approach of Armoured Fighting Vehicle (AFV) simulation is being presented in this research work. Combat Vehicles Research and Development Establishment (CVRDE) is planning to incorporate the concept of Distributed Interactive Simulation (DIS) for Armoured fighting vehicle training simulators. DIS is an open standard for conducting real-time platform-level war gaming across multiple host computers. This is used worldwide, especially by military organizations and other agencies such as those involved in space exploration and medicine. In a Distributed Interactive scenario, there is a need for a centre node to forward data to the receivers based on their respective priorities. A protocol called Predict-and-Quantize for Priority with directed minimum Spanning Tree (PQPST) is used effectively to predict priorities for the receivers. The predicted priorities are quantized to build a significant distribution trees for effective utilization of network bandwidth. A case study was attempted by CVRDE using the existing Troop training simulator architecture to compare the performance analysis of PST and PQPST algorithms and the results are present in this paper. This study reveals that PQPST algorithm is more suited for utilizing the network bandwidth and reduces the latency to achieve the real time response in the training simulator. This study also helps for the robotic applications to select the appropriate route in an intelligent manner from the network distribution graph.
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