Using modification of visibility-graph in solving the problem of finding shortest path for robot

2017 ◽  
Author(s):  
Tran Quoc Toan ◽  
A. A. Sorokin ◽  
Vo Thi Huyen Trang
Keyword(s):  
Shortest Path ◽  
Visibility Graph

scholarly journals TPA: A Two-Phase Approach Using Simulated Annealing for the Optimization of Census Taker Routes in Mexico

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Silvia Gaona ◽  
David Romero
Keyword(s):  
Simulated Annealing ◽  
Shortest Path ◽  
Travel Distance ◽  
Visibility Graph ◽  
Second Phase ◽  
Two Phase ◽  
Phase Approach ◽  
Dijkstra's Algorithm ◽  
Average Improvement ◽  
Parameter Values

Censuses in Mexico are taken by the National Institute of Statistics and Geography (INEGI). In this paper a Two-Phase Approach (TPA) to optimize the routes of INEGI’s census takers is presented. For each pollster, in the first phase, a route is produced by means of the Simulated Annealing (SA) heuristic, which attempts to minimize the travel distance subject to particular constraints. Whenever the route is unrealizable, it is made realizable in the second phase by constructing a visibility graph for each obstacle and applying Dijkstra’s algorithm to determine the shortest path in this graph. A tuning methodology based on theiracepackage was used to determine the parameter values for TPA on a subset of 150 instances provided by INEGI. The practical effectiveness of TPA was assessed on another subset of 1962 instances, comparing its performance with that of the in-use heuristic (INEGIH). The results show that TPA clearly outperformsINEGIH. The average improvement is of 47.11%.

Three-Dimensional Shortest Path Planning in the Presence of Polyhedral Obstacles

1996 ◽  
Vol 210 (4) ◽  
pp. 373-381 ◽  
Author(s):  
K Jiang ◽  
L D Seneviratne ◽  
S W E Earles
Keyword(s):  
Computational Complexity ◽  
Path Planning ◽  
Shortest Path ◽  
Turning Points ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Visibility Graph ◽  
Three Dimensions ◽  
View Point ◽  
Polyhedral Obstacles

A new algorithm is presented for solving the three-dimensional shortest path planning (3DSP) problem for a point object moving among convex polyhedral obstacles. It is the first non-approximate three-dimensional path planing algorithm that can deal with more than two polyhedral obstacles. The algorithm extends the visibility graph concept from two dimensions to three dimensions. The two main problems with 3DSP are identifying the edge sequence the shortest path passes through and the turning points of the shortest path. A technique based on projective relationships is presented for identifying the set of visible boundary edges (VBE) corresponding to a given view point over which the shortest path, from the view point to the goal, will pass. VBE are used to construct an initial reduced visibility graph (RVG). Optimization is used to revise the position of the turning points and hence the three-dimensional RVG (3DRVG) and the global shortest path is then selected from the 3DRVG. The algorithm is of computational complexity O(n3vk), where n is the number of verticles, v is the maximum number of vertices on any one obstacle and k is the number of obstacles. The algorithm is applicable only with polyhedral obstacles, as the theorems developed for searching for the turning points of the three-dimensional shortest path are based on straight edges of the obstacles. It needs to be further developed for dealing with arbitrary-shaped obstacles and this would increase the computational complexity. The algorithm is tested using computer simulations and some results are presented.

scholarly journals Visibility Graph Based Community Detection for Biological Time Series

2020 ◽  
Author(s):  
Minzhang Zheng ◽  
Sergii Domanskyi ◽  
Carlo Piermarocchi ◽  
George I. Mias
Keyword(s):  
Time Series ◽  
Community Detection ◽  
Shortest Path ◽  
Detection Algorithm ◽  
Visibility Graph ◽  
Main Stem ◽  
Biological Time ◽  
Link Type ◽  
Community Detection Algorithm ◽  
Biological Patterns

AbstractMotivationTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic temporal patterns or events as network communities. General methods to detect communities have used metrics to compare the connectivity within a community to the connectivity one would expect in a random model, or assumed a known number of communities, or are based on the betweenness centrality of edges or nodes. However, such methods were not specifically designed for network representations of time series. We introduce a visibility-graph-based method to build networks from different kinds of biological time series and detect temporal communities within these networks.ResultsTo characterize the uneven sampling of typical experimentally obtained biological time series, and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG) for time series. To detect communities, we first find the shortest path of the network between start and end nodes to identify nodes which have high intensities. This identifies the main stem of our community detection algorithm. Then, we aggregate nodes outside the shortest path to the nodes found on the main stem based on the closest path length. Through simulation, we demonstrate the validity of our method in detecting community structures on various networks derived from simulated time series. We also confirm its effectiveness in revealing temporal communities in experimental biological time series. Our results suggest our method of visibility graph based community detection can be effective in detecting temporal biological patterns.AvailabilityThe methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (https://doi.org/10.5281/zenodo.3691912) with documentation at https://pyiomica.readthedocs.io/en/latest/. The dataset and codes we used in this manuscript are publicly available at https://doi.org/10.5281/[email protected]

scholarly journals Visibility graph based temporal community detection with applications in biological time series

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Minzhang Zheng ◽  
Sergii Domanskyi ◽  
Carlo Piermarocchi ◽  
George I. Mias
Keyword(s):  
Time Series ◽  
Community Detection ◽  
Shortest Path ◽  
Detection Algorithm ◽  
Visibility Graph ◽  
Main Stem ◽  
Detection Methods ◽  
Biological Time ◽  
Dual Perspective ◽  
Community Detection Algorithm

AbstractTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

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