A novel heuristic pathfinding algorithm for 3D security modeling and vulnerability assessment  

作　　者：Jun Yang Yue-Ming Hong Yu-Ming Lv Hao-Ming Ma Wen-Lin Wang 

机构地区：[1]School of Electric Power Engineering,South China University of Technology,381 Wushan Road,Guangzhou 510640,China [2]Sino-German College of Intelligent Manufacturing,Shenzhen Technology University,Shenzhen 518118,China

出　　处：《Nuclear Science and Techniques》2025年第5期152-166,共15页核技术(英文)

基　　金：supported by the fundings from 2024 Young Talents Program for Science and Technology Thinking Tanks(No.XMSB20240711041);2024 Student Research Program on Dynamic Simulation and Force-on-Force Exercise of Nuclear Security in 3D Interactive Environment Using Reinforcement Learning,Natural Science Foundation of Top Talent of SZTU(No.GDRC202407);Shenzhen Science and Technology Program(No.KCXFZ20240903092603005);Shenzhen Science and Technology Program(No.JCYJ20241202124703004);Shenzhen Science and Technology Program(No.KJZD20230923114117032)。

摘　　要：Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulnerabilities and threats to the security and resilience of critical infrastructures.However,achieving efficient path optimization in complex large-scale three-dimensional(3D)scenes remains a significant challenge for vulnerability assessment.This paper introduces a novel A^(*)-algorithmic framework for 3D security modeling and vulnerability assessment.Within this framework,the 3D facility models were first developed in 3ds Max and then incorporated into Unity for A^(*)heuristic pathfinding.The A^(*)-heuristic pathfinding algorithm was implemented with a geometric probability model to refine the detection and distance fields and achieve a rational approximation of the cost to reach the goal.An admissible heuristic is ensured by incorporating the minimum probability of detection(P_(D)^(min))and diagonal distance to estimate the heuristic function.The 3D A^(*)heuristic search was demonstrated using a hypothetical laboratory facility,where a comparison was also carried out between the A^(*)and Dijkstra algorithms for optimal path identification.Comparative results indicate that the proposed A^(*)-heuristic algorithm effectively identifies the most vulnerable adversarial pathfinding with high efficiency.Finally,the paper discusses hidden phenomena and open issues in efficient 3D pathfinding for security applications.

关 键 词：Physical protection system 3D modeling and simulation Vulnerability assessment A^(*)Heuristic Pathfinding Dijkstra algorithm 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]