Application of A* algorithm in intelligent vehicle path planning

Abstract

Path planning is one of the important directions in the field of intelligent vehicles research. Traditional path planning algorithms generally use Dijkstra algorithm, Breadth-First-Search (BFS) algorithm and A* algorithm. Dijkstra algorithm is a search-based algorithm, which can search to an optimal path, but the disadvantage is too many expansion nodes, which leads to insufficient search efficiency. BFS algorithm is a heuristic search algorithm, which reduces the disadvantage of too many expansion nodes and improves the search efficiency by heuristic function. A* algorithm is a heuristic search algorithm that combines Dijkstra's algorithm and BFS algorithm, which has higher search efficiency and can search to an optimal path at the same time, but it is still lacking in the search mode and smoothness of the planned route. This paper first introduces the general path planning algorithm, then introduces and analyzes the A* algorithm, and proposes improvement measures for its shortcomings; finally, the executability and effectiveness of the improved algorithm are tested using simulation, and compared with the traditional A* algorithm, and the results show that the improved A* algorithm has good effect on path planning of intelligent vehicles.

Research on the Car Searching System in the Multi-Storey Garage with the RSSI Indoor Locating Based on Neural Network

“…At the same time, the terminal sends the WIFI signal source information within range to the server, and uses the RSSI fingerprint information for positioning [23,24]. The point information of the client and the target parking space is planned [25,26], the optimal path information is returned to the client, and the data are refreshed in real-time to achieve the effect of real-time positioning.…”

Research on preprocessing algorithm of indoor map partitioning and global path planning based on FAST

“…This paper takes global path planning as the research object, realizes map partitioning through key points, and reduces the search scope of global path planning to achieve the purpose of greatly reducing the calculation amount of the global path planning algorithm. At the same time, this paper uses A* [16], bidirectional A* [17], JPS [18], Dijkstra [19] algorithms based on graph search [20], PRM [21], and RRT [22] based on sampling [23] Algorithms are evaluated.. This approach optimizes the performance of the global path planning algorithm, limiting planning and navigation tasks to the necessary subregions and reducing computational time.…”