Implementation of UAV Smooth Path Planning by Improved Parallel Genetic Algorithm on Controller Area Network

2022 ◽  
Vol 35 (2) ◽  
Author(s):  
Vahid Jamshidi ◽  
Vahab Nekoukar ◽  
Mohammad Hossein Refan
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Controller Area Network ◽  
Area Network ◽  
Parallel Genetic Algorithm ◽  
Smooth Path

A GPU-Enabled Parallel Genetic Algorithm for Path Planning of Robotic Operators

2014 ◽  
pp. 1-13 ◽  
Author(s):  
Panpan Cai ◽  
Yiyu Cai ◽  
Indhumathi Chandrasekaran ◽  
Jianmin Zheng
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Parallel Genetic Algorithm

Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm

Robotica ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 823-836 ◽  
Author(s):  
Hamed Shorakaei ◽  
Mojtaba Vahdani ◽  
Babak Imani ◽  
Ali. Gholami
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Cost Function ◽  
Simple Formula ◽  
Parallel Genetic Algorithm ◽  
Probability Map ◽  
Genetic Encoding ◽  
Cell Position ◽  
The Cost ◽  
Unique Vector

SUMMARYThe current paper presents a path planning method based on probability maps and uses a new genetic algorithm for a group of UAVs. The probability map consists of cells that display the probability which the UAV will not encounter a hostile threat. The probability map is defined by three events. The obstacles are modeled in the probability map, as well. The cost function is defined such that all cells are surveyed in the path track. The simple formula based on the unique vector is presented to find this cell position. Generally, the cost function is formed by two parts; one part for optimizing the path of each UAV and the other for preventing UAVs from collision. The first part is a combination of safety and length of path and the second part is formed by an exponential function. Then, the optimal paths of each UAV are obtained by the genetic algorithm in a parallel form. According to the dimensions of path planning, genetic encoding has two or three indices. A new genetic operator is introduced to select an appropriate pair of chromosome for crossover operation. The effectiveness of the method is shown by several simulations.

Research on Logistic Path Planning Strategy Based on Coarse-Grained Parallel Genetic Algorithm

2013 ◽  
Vol 798-799 ◽  
pp. 920-923
Author(s):  
Wu Xue Jiang ◽  
Xuan Zi Hu ◽  
Min Xia Liu ◽  
Peng Fei Yin
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Convergence Rate ◽  
Route Planning ◽  
Planning System ◽  
Coarse Grained ◽  
Parallel Genetic Algorithm ◽  
Planning Strategy ◽  
Elitist Strategy ◽  
Implementation Steps

In order to minimize the precocity and deceit happened in genetic algorithm, this thesis puts forward an improved intelligent evolutionary algorithmcoarse-grained parallel genetic algorithm which proposes schema order based cross operator, task immigration based cross operator, and improved approach for elitist strategy. Also, this paper applies the algorithm into logistic route planning system to clarify the concrete implementation steps for coding, population generation and genetic operator. According to experiment result, the improved algorithm mentioned in this assay have advanced the convergence rate and optimizing ability to some extent.

Parallel genetic algorithm based automatic path planning for crane lifting in complex environments

2016 ◽  
Vol 62 ◽  
pp. 133-147 ◽  
Author(s):  
Panpan Cai ◽  
Yiyu Cai ◽  
Indhumathi Chandrasekaran ◽  
Jianmin Zheng
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Complex Environments ◽  
Parallel Genetic Algorithm

Research on text feature clustering based on an improved parallel genetic algorithm

2015 ◽  
Author(s):  
M. Y. Jiang ◽  
X. J. Fan ◽  
Y. X. Zhou ◽  
J. Lian ◽  
J. Q. Jiang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Feature Clustering ◽  
Text Feature
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