The Shortest Path for a Point Passing Through Obstacles Represented by Quadratic Curves

1991 ◽  
Author(s):  
James L. Blechschmidt ◽  
J. L. Wu
Keyword(s):  
Shortest Path ◽  
Path Generation ◽  
Algebraic Representation ◽  
A Algorithm

Abstract In this paper we present an algorithm for the search of the shortest path through a set of obstacles approximated by circles and ellipses. A set of heuristics is developed to eliminate obstacles that do not affect the search for the shortest path. The A algorithm is used during the path generation phase to direct work toward the estimated shortest path. Algebraic techniques are used for computing a set of pseudo vertices since no natural vertices exist with the algebraic representation of the obstacles. Examples are given to demonstrate the techniques developed.

scholarly journals An Improved A-Star Algorithm Considering Water Current, Traffic Separation and Berthing for Vessel Path Planning

2019 ◽  
Vol 9 (6) ◽  
pp. 1057 ◽  
Author(s):  
Chenguang Liu ◽  
Qingzhou Mao ◽  
Xiumin Chu ◽  
Shuo Xie
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Optimal Path ◽  
Water Current ◽  
Path Generation ◽  
Collision Risk ◽  
A Algorithm ◽  
Risk Models ◽  
Planning Methods ◽  
Path Cost

A traditional A-Star (A*) algorithm generates an optimal path by minimizing the path cost. For a vessel, factors of path length, obstacle collision risk, traffic separation rule and manoeuvrability restriction should be all taken into account for path planning. Meanwhile, the water current also plays an important role in voyaging and berthing for vessels. In consideration of these defects of the traditional A-Star algorithm when it is used for vessel path planning, an improved A-Star algorithm has been proposed. To be specific, the risk models of obstacles (bridge pier, moored or anchored ship, port, shore, etc.) considering currents, traffic separation, berthing, manoeuvrability restriction have been built firstly. Then, the normal path generation and the berthing path generation with the proposed improved A-Star algorithm have been represented, respectively. Moreover, the problem of combining the normal path and the berthing path has been also solved. To verify the effectiveness of the proposed A-Star path planning methods, four cases have been studied in simulation and real scenarios. The results of experiments show that the proposed A-Star path planning methods can deal with the problems denoted in this article well, and realize the trade-off between the path length and the navigation safety.

Improvement A* Algorithm Based on Dynamic Consistency Assumption

2011 ◽  
Vol 97-98 ◽  
pp. 883-887
Author(s):  
Liang Zou ◽  
Zi Zhang ◽  
Ling Xiang Zhu
Keyword(s):  
Lower Bound ◽  
Shortest Path ◽  
Dynamic Networks ◽  
Shortest Paths ◽  
Dynamic Consistency ◽  
Destination Node ◽  
Shortest Path Algorithm ◽  
A Algorithm ◽  
Dynamic Form

Efficient dynamic shortest path algorithm in static networks plays an important role in ITS. To solve this problem, this paper brings forward the dynamic form of Consistency Assumption and Dynamic A* algorithm (A* algorithm based on dynamic lower bound, DA* algorithm) based on dynamic lower bound. DA* algorithm and the dynamic form of Consistency Assumption are described in detail. It is proved that DA* algorithm can solve one origin node to one destination node shortest paths problem in dynamic networks, if DA* algorithm’s dynamic lower bound satisfies the dynamic form of Consistency Assumption.

scholarly journals Implementation of A-Star Algorithm in Determining the Shortest Path on Graph

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Graph Theory ◽  
Greedy Algorithm ◽  
Shortest Path ◽  
Visual Representation ◽  
A Algorithm ◽  
Shortest Route

A* algorithm according to the inventor of a mathematical scientist Abu Abdullah Muhammad bin Musa al-Khwarizmi The Inventor of Algorithms is a greedy algorithm that is used to solve problems in determining the shortest path. This problem is often implemented in the form of a graph. Graph theory is a subject that is old but has many expectations to date. Graphs are used to discrete and relationship between these objects. The visual representation of the graph is to state that the object is declared as a circle. By using the shortest route determination application using A* algorithm, the application is suitable to be used to determine the optimization on the shortest route. However, it depends on the problems faced. Using this algorithm method will help efficient time search using processes that are mostly random, and produce good solutions at fast speeds.

scholarly journals OPTIMIZING THE CONTAINER TRUCK PATHS WITH UNCERTAIN TRAVEL TIME IN CONTAINER PORTS

Transport ◽  
2021 ◽  
Vol 36 (6) ◽  
pp. 444-462
Author(s):  
Jiaming Liu ◽  
Bin Yu ◽  
Wenxuan Shan ◽  
Baozhen Yao ◽  
Yao Sun
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Programming Model ◽  
Mixed Integer ◽  
A Algorithm ◽  
Shuffled Complex Evolution ◽  
Proposed Model ◽  
Container Ports ◽  
Yard Template

The yard template problem in container ports determines the assignment of space to store containers for the vessels, which could impact container truck paths. Actually, the travel time of container truck paths is uncertain. This paper considers the uncertainty from two perspectives: (1) the yard congestion in the context of yard truck interruptions, (2) the correlation among adjacent road sections (links). A mixed-integer programming model is proposed to minimize the travel time of container trucks. The reliable shortest path, which takes the correlation among links into account is firstly discussed. To settle the problem, a Shuffled Complex Evolution Approach (SCE-UA) algorithm is designed to work out the assignment of yard template, and the A* algorithm is presented to find the reliable shortest path according to the port operator’s attitude. In our case study, one yard in Dalian (China) container port is chosen to test the applicability of the model. The result shows the proposed model can save 9% of the travel time of container trucks, compared with the model without considering the correlation among adjacent links.

scholarly journals A hybrid technique for single-source shortest path-based on A* algorithm and ant colony optimization

2020 ◽  
Vol 9 (2) ◽  
pp. 356
Author(s):  
Sameer Alani ◽  
Atheer Baseel ◽  
Mustafa Maad Hamdi ◽  
Sami Abduljabbar Rashid
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Shortest Paths ◽  
Ant Colony ◽  
Fast Time ◽  
Hybrid Technique ◽  
Single Source ◽  
A Algorithm ◽  
A New Technique

<span lang="EN-US">In the single-source shortest path (SSSP) problem, the shortest paths from a source vertex v to all other vertices in a graph should be executed in the best way. A common algorithm to solve the (SSSP) is the A* and Ant colony optimization (ACO). However, the traditional A* is fast but not accurate because it doesn’t calculate all node's distance of the graph. Moreover, it is slow in path computation. In this paper, we propose a new technique that consists of a hybridizing of A* algorithm and ant colony optimization (ACO). This solution depends on applying the optimization on the best path. For justification, the proposed algorithm has been applied to the parking system as a case study to validate the proposed algorithm performance. First, A*algorithm generates the shortest path in fast time processing. ACO will optimize this path and output the best path. The result showed that the proposed solution provides an average decreasing time performance is 13.5%.</span>

Optimasi Pencarian Jalur dengan Metode A-Star

2013 ◽  
Vol 5 (2) ◽  
pp. 42-47
Author(s):  
Veronica Mutiana ◽  
Fitria Amastini ◽  
Noviana Mutiara
Keyword(s):  
Shortest Path ◽  
Path Selection ◽  
Traffic Density ◽  
Optimal Route ◽  
A Algorithm ◽  
Worst Case ◽  
Traffic Jam ◽  
Sensor Module ◽  
Using Data ◽  
High Level

High level of traffic density can lead to traffic jam those will make troublesome for driver to reach destination with alternative shortest path on time. Therefore, it is neccessary to make an agent that can choose optimal route without being stuck on traffic jam. In this paper, algorithm for choose optimal route is A* method for shortest path problem and use backtrack process when there is a traffic jam occurs on several roads. The design of algorithm is tested by using data which contain 100 locations or nodes and 158 roads or paths in Gading Serpong with an agent that can searching shortest path and sensor module that can send the traffic status based on number of vehicle on several particular node. Based on testing, A* method does not guarantee for path selection if agent is not full observable with environment and there is some case that can lead a worst case. Index Terms— A* Algorithm, Backtrack, Shortest Path, Traffic Density

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