Global Path Planning Based on an Improved A* Algorithm in ROS

2021 ◽  
pp. 1134-1144
Author(s):  
Desheng Feng ◽  
Lixia Deng ◽  
Tao Sun ◽  
HaiYing Liu ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
A Algorithm ◽  
Global Path Planning

scholarly journals An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles

2020 ◽  
Vol 73 (6) ◽  
pp. 1247-1262
Author(s):  
Yang Long ◽  
Zheming Zuo ◽  
Yixin Su ◽  
Jie Li ◽  
Huajun Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Path Planning ◽  
Optimal Parameter ◽  
A Algorithm ◽  
Unmanned Surface Vehicles ◽  
Parameter Combination ◽  
Bacterial Foraging ◽  
Optimisation Algorithm ◽  
Global Path Planning ◽  
Influential Parameters

The bacterial foraging optimisation (BFO) algorithm is a commonly adopted bio-inspired optimisation algorithm. However, BFO is not a proper choice in coping with continuous global path planning in the context of unmanned surface vehicles (USVs). In this paper, a grid partition-based BFO algorithm, named AS-BFO, is proposed to address this issue in which the enhancement is contributed by the involvement of the A* algorithm. The chemotaxis operation is redesigned in AS-BFO. Through repeated simulations, the relative optimal parameter combination of the proposed algorithm is obtained and the most influential parameters are identified by sensitivity analysis. The performance of AS-BFO is evaluated via five size grid maps and the results show that AS-BFO has advantages in USV global path planning.

Robotic Fish Path Planning Based on an Improved A* Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 968-972 ◽  
Author(s):  
Huan Wang ◽  
Yu Lian Jiang
Keyword(s):  
Path Planning ◽  
Complex Environment ◽  
A Algorithm ◽  
Global Path Planning ◽  
Local Path Planning ◽  
Search Data ◽  
Infinite Loop ◽  
Transition Points ◽  
Local Path ◽  
Divide And Rule

Applying the global path planning to traditional A* algorithm in a complex environment and a lot of obstacles will result in an infinite loop because there are too many search data. To resolve this problem, this paper provides a new divide-and-rule path planning method which is based on improved A* algorithm. It uses several transition points to divide the entire grid map areas into several sub-regions. We set different speeds in each sub-region for local path planning. Thus the complex global path planning is turned into some simple local path planning. It reduces the search data of A* algorithm and avoids falling into the infinite loop. By this method, this paper designs the path planning of heading the ball, and smoothes the orbit. The simulation results show that the improved A* algorithm is better and more effective than the traditional one.

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