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Workspace Modeling and Path Planning for Truss Structure Inspection by Unmanned Aircraft

工作区当模特儿并且路径计划为由无人的飞机捆绑结构检查

作     者:Das, Arun Woolsey, Craig A. 

作者机构:Tech Univ Munich Elect Engn D-80333 Munich Germany Virginia Polytech Inst & State Univ Crofton Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA 

出 版 物:《JOURNAL OF AEROSPACE INFORMATION SYSTEMS》 (航天信息系统杂志)

年 卷 期:2019年第16卷第1期

页      面:37-51页

核心收录:

学科分类:08[工学] 0825[工学-航空宇航科学与技术] 

基  金:Virginia Tech's Institute for Critical Technology and Applied Science National Science Foundation (NSF) [IIS-1840044] Center for Unmanned Aircraft Systems, an NSF Industry/University Cooperative Research Center [IIP-1539975, CNS-1650465] 

主  题:Trusses Path Planning Unmanned Aircraft Small Unmanned Aircraft System Cantilever Beam Algorithm Performance Particle Swarm Optimization Flight Control System Multirotor Robots 

摘      要:Small unmanned aircraft systems can increase efficiency and reduce risk to humans during the inspection of truss-supported structures such as steel bridges. This paper describes a method to mathematically represent a truss and subsequently to plan an efficient collision-free inspection path. The algorithm checks user-defined perspectives for feasibility and redefines any infeasible perspectives. A deterministic roadmap is then generated as the complete graph over these perspectives, and some additional nodes are generated near the joints of the structure. A traveling salesman problem (TSP) is solved to find an efficient inspection path that tours all perspective points, and a local A* path planner then refines the inspection path to circumvent obstructions. The TSP is re-solved, with an updated cost based on local detours;and the process iterates until a feasible TSP solution is found. The lazy approach to global and local planning, for which paths are checked for feasibility after the fact and amended if necessary, ensures quick convergence to an efficient inspection path. Simulation results show the functionality of the algorithm. Comparison with a probabilistic roadmap method indicates the proposed algorithm s efficiency.

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