scholarly journals An improved hierarchical A * algorithm in the optimization of parking lots

2017 ◽  
Author(s):  
Yong Wang ◽  
Junjuan Wu ◽  
Ying Wang
Keyword(s):  
A Algorithm ◽  
Parking Lots

Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

Proposal and implementation of parking guidance method

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 12-14
Author(s):  
Akira Kawai ◽  
Masahiro Kenmotsu
Keyword(s):  
Traffic Congestion ◽  
Positional Information ◽  
Associate Professor ◽  
Parking Lots ◽  
Gps Technology ◽  
The World ◽  
Car Park ◽  
Free Spaces ◽  
Interest In Research ◽  
Gain Access

Traffic congestion in parking lots is a common phenomenon across the world and larger commercial facilities with multiple parking areas may be particularly affected as many users struggle to gain access to sought-after parking spots close to their destinations. These popular zones often see traffic jams forming as many vehicles arrive within these regions, while less popular areas may remain free from congestion. This creates a very uneven distribution of traffic, with motorists in popular areas becoming trapped and unable to leave bottleneck regions. As a result, the car park management industry has taken an interest in research into parking guidance. Parking guidance has been developed to help improve efficiencies in car parks, guiding drivers to specific spaces using GPS technology to highlight free spaces near their location detailing the most efficient way to get to that spot. Associate Professor Akira Kawai, who is based at Shiga University in Japan, has been working on a KAKEN project that seeks to leverage real-time positional information to help guide drivers to free spaces within parking lots.

scholarly journals Global Dynamic Path Planning Fusion Algorithm Combining Jump-A* Algorithm and Dynamic Window Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 19632-19638
Author(s):  
Lisang Liu ◽  
Jinxin Yao ◽  
Dongwei He ◽  
Jian Chen ◽  
Jing Huang ◽  
...  
Keyword(s):  
Path Planning ◽  
A Algorithm ◽  
Fusion Algorithm ◽  
Global Dynamic ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Window Approach

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

Sign in / Sign up

Export Citation Format

Share Document