Optimal Path Finding using Iterative SARSA

Author(s):  
Prajval Mohan ◽  
Lakshya Sharma ◽  
Pranav Narayan
Keyword(s):  
Author(s):  
Pei Cao ◽  
Zhaoyan Fan ◽  
Robert X. Gao ◽  
J. Tang

This research aims at unleashing the potential of additive manufacturing technology in industrial design that can produce structures/devices with irregular component geometries to reduce sizes/weights. We explore, by means of path-finding, the length minimization of freeform hydraulic piping network in compact space under given constraints. Previous studies on path-finding have mainly focused on enhancing computational efficiency due to the need to produce rapid results in such as navigation and video-game applications. In this research, we develop a new Focal Any-Angle A* approach that combines the merits of grid-based method and visibility graph-based method. Specifically, we formulate pruned visibility graphs preserving only the useful portion of the vertices and then find the optimal path based on the candidate vertices using A*. The reduced visibility graphs enable us to outperform approximations and maintain the optimality of exact algorithms in a more efficient manner. The algorithm proposed is compared to the traditional A* on Grids, Theta* and A* on visibility graphs in terms of path length, number of nodes evaluated, as well as computational time. As demonstrated and validated through case studies, the proposed method is capable of finding the shortest path with tractable computational cost, which provides a viable design tool for the additive manufacturing of piping network systems.


2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Liang Shen ◽  
Hu Shao ◽  
Long Zhang ◽  
Jian Zhao

There is a growing interest in finding a global optimal path in transportation networks particularly when the network suffers from unexpected disturbance. This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic. Traditional path finding methods based on least expected travel time cannot capture the network user’s risk-taking behaviors in path finding. To overcome such limitation, the reliable path finding algorithms have been proposed but the convergence of global optimum is seldom addressed in the literature. This paper integrates the K-shortest path algorithm into Backtracking method to propose a new path finding algorithm under uncertainty. The global optimum of the proposed method can be guaranteed. Numerical examples are conducted to demonstrate the correctness and efficiency of the proposed algorithm.


2016 ◽  
Vol 19 (4) ◽  
pp. 2179-2188 ◽  
Author(s):  
Bin Hu ◽  
Huan-yan Qian ◽  
Yi Shen ◽  
Jia-xing Yan

2013 ◽  
Vol 03 (04) ◽  
pp. 260-265 ◽  
Author(s):  
Zhanquan Sun ◽  
Weidong Gu ◽  
Yanling Zhao ◽  
Chunmei Wang

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 15459-15471 ◽  
Author(s):  
Xiangyuan Jiang ◽  
Zongyuan Lin ◽  
Tianhao He ◽  
Xiaojing Ma ◽  
Sile Ma ◽  
...  

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