Unobstructed Shortest Paths in Polyhedral Environments

Unobstructed Shortest Paths in Polyhedral Environments
Free download. Book file PDF easily for everyone and every device. You can download and read online Unobstructed Shortest Paths in Polyhedral Environments file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Unobstructed Shortest Paths in Polyhedral Environments book. Happy reading Unobstructed Shortest Paths in Polyhedral Environments Bookeveryone. Download file Free Book PDF Unobstructed Shortest Paths in Polyhedral Environments at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Unobstructed Shortest Paths in Polyhedral Environments Pocket Guide. Theorem 1. The time complexity of the algorithm in the triangle mesh G is O n log n , where n denotes the number of triangles. Proof: We can generate Delaunay triangulation as a triangle mesh with time of O n log n [ 22 ], as a result, time complexity in Step 1 is O n log n [ 23 ]. We therefore know that all the time complexity for Steps 2 and 3 are O n. Therefore, the overall time complexity for PathShortening is O n log n. Theorem 2. The space complexity of constructing the triangle mesh in the quadratic surface is O n , where n is the number of triangles.

Therefore, the space complexity is bounded by O n. Hence, the space complexity is O n.

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Presents algebraic and geometric algorithms to deal with a specific problem, which frequently occurs in model-based robotics systems and is of utmost. Unobstructed Shortest Paths in Polyhedral Environments (Lecture Notes in Computer Science) [Varol Akman] on *FREE* shipping on qualifying.

The performance of Delaunay triangulation-based path algorithm has been analysed for evaluating the near-shortest path with several real GIS maps in the Matlab Language. Figure 3 shows one of the experimental results with a GIS map, where the solid line is the near-shortest path and dashed lines are the shortcuts. Near-shortest path searching with a GIS map.

Figure 4 a and b illustrates the average running time and path length between two algorithms. When compared to one Steiner point, the average path length difference of the Delaunay triangulation-based algorithm is 6.

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When it increased three Steiner points, the length difference is only 0. This proves that the Delaunay triangulation-based algorithm can solve the NP-hard problem and also obtain fast computing features. This section explains an application that benefits from the Delaunay triangulation-based algorithm.

Actually, it can be applied to shortest path planning for Mars rover and mission planning for cruise missiles in the quadric surface. To verify the correctness and performance, we assume a cruise missile needs to move from the source position S to the destination position D , as shown in Figure 6 b. Once the virtual altitude and thresholds are applied, a shortest path is obtained.

An illustration of the shortest path for planning a cruise missile on the landscape. In this chapter, an O n log n time near-shortest path planning based on the Delaunay triangulation, the Ahuja-Dijkstra algorithm, and ridge points on the quadric surface are introduced. Although the length of path obtained by Delaunay triangulation-based algorithm is 0.

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Therefore, the Delaunay triangulation-based algorithm presents a good near-shortest path searching solution in the quadric surface with a very short amount of computation time. Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.

Unobstructed Shortest Paths in Polyhedral Environments (Paperback)

Help us write another book on this subject and reach those readers. Login to your personal dashboard for more detailed statistics on your publications. We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Downloaded: Abstract In this chapter, recent near-shortest path-planning algorithms with O nlog n in the quadric plane based on the Delaunay triangulation, Ahuja-Dijkstra algorithm, and ridge points are reviewed.

Keywords Delaunay triangulation Dijkstra algorithm ridge point near-shortest path mission planning NP-hard. Introduction In the Euclidean plane with obstacles, the shortest path problem is to find an optimal path between source and destination. Table 1. Algorithm and illustration In this section, a Delaunay triangulation-based method will combine the concepts of Ahuja-Dijkstra algorithm and ridge points to construct a directed graph and to obtain the shortest possible path length on the quadratic surfaces.

Performance analysis A near-shortest path algorithm on the Quadratic surface is the fastest in the literature. Experimental result The performance of Delaunay triangulation-based path algorithm has been analysed for evaluating the near-shortest path with several real GIS maps in the Matlab Language. Computational Geometry 77 , Information Sciences , Journal of Manufacturing Science and Engineering Approaches for Clustering Polygonal Obstacles. Information Technology - New Generations, Combinatorial Optimization and Applications, European Journal of Operational Research :1, Robotics and Autonomous Systems 93 , Simulating Heterogeneous Crowd with Interactive Behaviors, Geographical Analysis 48 :2, The International Journal of Robotics Research 35 :5, Automatica 65 , Geometric Shortest Paths in the Plane.

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Encyclopedia of Algorithms, Computational Geometry 48 :9, GeoInformatica 19 :3, ACM Transactions on Algorithms 11 :4, Algorithmic Foundations of Robotics XI, Algorithms and Computation, Theoretical Computer Science , ACM Transactions on Graphics 33 :5, Discrete Applied Mathematics , Journal of Computing and Information Science in Engineering 14 Computational Geometry.

Computing Handbook, Third Edition, Algorithmica 69 :1, Computer-Aided Design 48 , Siu-Wing Cheng and Jiongxin Jin. International Journal of Geographical Information Science 27 , Applied and Computational Harmonic Analysis 35 :1, Danny Z. Chen , John Hershberger , and Haitao Wang. Computational Geometry 46 :1, Handbook of Combinatorial Optimization, Explaining Algorithms Using Metaphors, Geodesic-Preserving Polygon Simplification.

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