scholarly journals Entropy-based optimization via A* algorithm for parking space recommendation

2021 ◽  
Author(s):  
Xin Wei ◽  
Runqi Qiu ◽  
Houyu Yu ◽  
Yurun Yang ◽  
Haoyu Tian ◽  
...  
Keyword(s):  
Parking Space ◽  
A Algorithm

Application of CAN Bus and the Layered A* Algorithm in the Parking Guidance System

2014 ◽  
Vol 602-605 ◽  
pp. 887-890
Author(s):  
Li Ping Cheng ◽  
Bo Yan ◽  
Yong Hai Tan
Keyword(s):  
Design Method ◽  
Optimal Path ◽  
Guidance System ◽  
Parking Space ◽  
A Algorithm ◽  
Network Nodes ◽  
Path Information ◽  
Parking Lot ◽  
Vehicle Information ◽  
In Virtue Of

Most parking guidance system is not perfect, in an unknown and complex parking lot, the most urgent problem to the driver is how to find a parking space and find his car from the parking space quickly. In order to solve this problem, in virtue of the improved layered A* algorithm, system fast calculates the optimal path information which is from the entrance to each parking space and from each parking space to the export. Compared with using A* algorithm directly, the calculation time is shorten, the number of traversal nodes is reduced. At the same time, it adopts the integration design idea in the design of hardware and software, and uses international popular CAN to complete the design of network nodes in the parking guidance system, in which the monitoring interface is designed, the parking space information and gateway vehicle information are collected, the optimal path information are displayed. As integration design method was adopted, the design contents are simplified, the development hours are shortened, and the CAN intelligent nodes have a good extendibility and portability. This is the foundation for the expansion of parking lot In the future.

Study on Parking Space Index of Typical Buildings in Weihai

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Peiyu Jiang ◽  
Xu Wang ◽  
Zhangyu Han
Keyword(s):  
Parking Space

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.

Developing A Vision-based Adaptive Parking Space Management System

2016 ◽  
Vol 6 (3) ◽  
pp. 192-200 ◽  
Author(s):  
Karim Hammoudi ◽  
Halim Benhabiles ◽  
Abhishek Jandial ◽  
Fadi Dornaika ◽  
Joseph Mouzna
Keyword(s):  
Management System ◽  
Parking Space ◽  
Space Management

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.

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