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A Constraint Programming Solution for the Military Unit Path Finding Problem
Published in Jitendra R. Raol, Ajith K. Gopal, Mobile Intelligent Autonomous Systems, 2016
Louise Leenen, Alexander Terlunen, Herman le Roux
Path finding is the problem of moving an object from a starting point to a destination point, while avoiding obstacles and minimizing costs. It has many applications ranging from computer games, transportation, robotics, networks and others. Path finding algorithms can be divided into two different types, static (or global) and dynamic algorithms. For a static algorithm, the environment is assumed to be known and an optimal path is calculated before the object is moved. For a dynamic algorithm, the environment may not be completely known before the object starts moving, or it may change while the object is moving. In such a case, the path plan has to be updated while it is being executed. The path finding problem has been well studied and there is a vast body of research in the literature.
Assessing the quantitative description of the accessibility of buildings in the University of the Philippines Diliman (UPD)
Published in Shin-ya Nishizaki, Masayuki Numao, Jaime Caro, Merlin Teodosia Suarez, Theory and Practice of Computation, 2019
J.M. Choa, P.G. Santiago, A.A. Cariaga, R.P. Feria, L.L. Figueroa, R.C. Solamo
Since there are no available or obtainable resources regarding the layout of each building to be included in the application, building rooms were surveyed manually. Graph representations of the buildings to be used were constructed in the particular Java implementation of the A* algorithm. Each room, staircase, and notable landmark was represented as a node in the graph, with appropriate adjacency taken as if the node were the origin of a path-finding run. Dijkstra’s algorithm, a variant of the A* algorithm, was selected as a balance between optimal path-finding and optimal running time.
Revealing the true navigability of the Northern Sea Route from ice conditions and weather observations
Published in Maritime Policy & Management, 2023
Gleb Sibul, Peihao Yang, Dmitri Muravev, Jian Gang Jin, Linghe Kong
As for algorithms, most of the authors used simple and reliable pathfinding algorithms such as A* and Dijkstra (Dijkstra 1959; Hart, Nilsson, and Raphael 1968). However, some authors used less trivial solutions, such as Powell’s method (Kotovirta et al. 2009), finding the most dangerous ice areas using Voronoi Diagrams and their bypassing (Liu, Sattar, and Songnian 2016), wave-based routing (Topaj et al. 2019), stochastic dynamic programming (Schutz 2011), conjugate direction method (Kotovirta et al. 2009), neural networks (Zhang et al. 2019), solving differential equations of motion (Montewka et al. 2019), genetic algorithms (Kuhlemann and Tierney 2020), and even finite element method (Piehl, Milaković, and Ehlers 2017). The most popular type of objective function was travel time minimization. It was achieved by sailing through areas with the least thin ice, which also benefited sailing safety. Knowing vessel sailing time, some authors determined trip cost using economic parameters such as operational cost, freight rate, fuel cost, capital cost, etc (Mulherin et al. 1996; Nam et al. 2013; Wang, Zhang, and Qian 2018; Topaj et al. 2019). Most authors argue that the quality of algorithms depends on the quality, accuracy, and resolution of the input data. Therefore, some researchers (Choi et al. 2015; Wang, Zhang, and Qian 2018) suggested using statistical methods and considered input parameters deviations in their models.
Scheduling of autonomous mobile robots with conflict-free routes utilising contextual-bandit-based local search
Published in International Journal of Production Research, 2022
Sungbum Jun, Chul Hun Choi, Seokcheon Lee
Nevertheless, many hurdles, such as the computational complexity of optimisation problems for scheduling and routing, must be cleared in order to make the best use of AMRs. AMR optimisation problems can be classified into three sub-problems: path finding, vehicle routing, and conflict resolution. The goal of the path finding problem is to find the shortest path between two locations. In the case of the vehicle routing problem, the objective is to determine the best allocation of pickup and delivery operations with consideration of the paths calculated in the path finding problem. In terms of conflict resolution, the main purpose is to minimise collisions or delays based on the vehicle routing and path finding solutions. All these sub-problems are not independent, but rather are closely related to each other. For example, to find the best path of an AMR, the required distance between two locations as well as the routes of the other vehicles must be considered. Also, the expected delivery time largely depends on delays caused by conflicts between AMRs.
Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data
Published in International Journal of Digital Earth, 2022
Adan Wu, Tao Che, Xin Li, Xiaowen Zhu
Pathfinding is to find a path with different objective functions, like distance, estimated time of arrival (ETA) and fuel consumption. The Dijkstra algorithm (Dijkstra 1959), A* algorithm (Hart, Nilsson, and Raphael 1972) and ant colony algorithm (ACA) (Dorigo, Birattari, and Stutzle 2006) are traditional, widely used pathfinding algorithms. We summarize the application cases of these three algorithms for AR planning in Table 1. These algorithms have been successfully used to identify an optimal route that best meets certain criteria (the shortest, least costly, fastest, etc.) between two points in a large network.