Grid-based RRT* for minimum dose walking path-planning in complex radioactive environments

被引:54
作者
Chao, Nan [1 ]
Liu, Yong-kuo [1 ]
Xia, Hong [1 ]
Ayodeji, Abiodun [1 ,2 ]
Bai, Lu [1 ]
机构
[1] Harbin Engn Univ, Fundamental Sci Nucl Safety & Simulat Technol Lab, Harbin 150001, Heilongjiang, Peoples R China
[2] Nigeria Atom Energy Commiss, Nucl Power Plant Dev Directorate, Abuja, Nigeria
基金
中国国家自然科学基金;
关键词
Minimum dose path-planning; Sampling-based algorithm; Radiation safety; Nuclear facilities; LOCALIZED NAVIGATION ALGORITHM; SAMPLING-BASED ALGORITHMS; NUCLEAR-FACILITIES; RADIATION EVASION; CRITERION;
D O I
10.1016/j.anucene.2018.01.007
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
This paper develops an approach to provide path navigation with minimum dose for occupational workers in nuclear facilities to increase personnel safety. An algorithm called GB-RRT* is proposed by combining the principle of the rapidly exploring random tree star (RRT*) with the grid searching strategy. The proposed hybridized algorithm compensates for the weaknesses of RRT* with the strengths of the grid search strategy, and is applicable in complex environments with obstacles and narrow areas without relying on pre-designed road networks. Simulation results of the proposed algorithm under three radioactive environments with sparse obstacles, area cluttered with obstacles, and narrow areas are compared with those derived from RRT*. The results show that the GB-RRT* performs better than RRT* in both convergence and reliability toward achieving the minimum dose path. Hence, we present a more reliable and efficient for providing the minimum dose path for occupational workers especially in complex radioactive environments with clutter obstacles and narrow areas. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:73 / 82
页数:10
相关论文
共 17 条
[1]  
[Anonymous], 2014, INT J ROB RES
[2]  
[Anonymous], 2009, THESIS
[3]   A sampling-based method with virtual reality technology to provide minimum dose path navigation for occupational workers in nuclear facilities [J].
Chao, Nan ;
Liu, Yong-kuo ;
Xia, Hong ;
Xie, Chun-li ;
Ayodeji, Abiodun ;
Yang, Huan ;
Bai, Lu .
PROGRESS IN NUCLEAR ENERGY, 2017, 100 :22-32
[4]   Optimal Path Planning in Complex Cost Spaces With Sampling-Based Algorithms [J].
Devaurs, Didier ;
Simeon, Thierry ;
Cortes, Juan .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2016, 13 (02) :415-424
[5]   Sparse roadmap spanners for asymptotically near-optimal motion planning [J].
Dobson, Andrew ;
Bekris, Kostas E. .
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2014, 33 (01) :18-47
[6]  
Islam F., 2012, 2012 IEEE International Conference on Mechatronics and Automation (ICMA), P1651, DOI 10.1109/ICMA.2012.6284384
[7]  
Jeon JH, 2011, IEEE DECIS CONTR P, P3276, DOI 10.1109/CDC.2011.6161521
[8]  
Karaman S, 2011, IEEE INT CONF ROBOT, P1478
[9]   Sampling-based algorithms for optimal motion planning [J].
Karaman, Sertac ;
Frazzoli, Emilio .
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2011, 30 (07) :846-894
[10]  
Khasawneh M. A., 2009, INT S MECH ITS APPL, P1