Unmanned vehicle path planning using a novel ant colony algorithm

The ant colony optimization algorithm is an effective way to solve the problem of unmanned vehicle path planning. First, establish the environment model of the unmanned vehicle path planning, process and describe the environmental information, and finally realize the division of the problem space. N...

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Veröffentlicht in:EURASIP journal on wireless communications and networking Jg. 2019; H. 1; S. 1 - 9
Hauptverfasser: Yue, Longwang, Chen, Hanning
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 28.05.2019
Springer Nature B.V
SpringerOpen
Schlagworte:
Algorithms
Ant colony algorithm
Ant colony optimization
Biomimetics
Communications Engineering
Distributed Big Data Processing in SCADA Communication Networks
Engineering
Environment models
Grid method
Information Systems Applications (incl.Internet)
Networks
Path planning
Penalty strategy
Product design
Signal,Image and Speech Processing
Unmanned vehicles
Volatility
Path planning
Penalty strategy
Grid method
Ant colony algorithm
ISSN:1687-1499, 1687-1472, 1687-1499
Online-Zugang:Volltext
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Zusammenfassung:The ant colony optimization algorithm is an effective way to solve the problem of unmanned vehicle path planning. First, establish the environment model of the unmanned vehicle path planning, process and describe the environmental information, and finally realize the division of the problem space. Next, the biomimetic behavior of the ant colony algorithm is described. The ant colony algorithm has been improved by adding a penalty strategy. This penalty strategy can enhance the utilization of resources and guide the ants to explore other unknown areas by using the worse value in the search history to enhance the volatility of the pheromone.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1687-1499
1687-1472
1687-1499
DOI:10.1186/s13638-019-1474-5