scholarly journals UNMANNED AIR VEHICLE PATH PLANNING FOR MARITIME SURVEILLANCE USING CLUSTER-BASE METHOD

Aviation ◽  
2021 ◽  
Vol 25 (3) ◽  
pp. 211-219
Author(s):  
Prasetyo Ardi Probo Suseno ◽  
Try Kusuma Wardana
Keyword(s):  
Path Planning ◽  
Clustering Methods ◽  
Fishing Activity ◽  
Main Method ◽  
Maritime Surveillance ◽  
Unmanned Air Vehicle ◽  
Air Vehicle ◽  
Base Method ◽  
Recorded Data ◽  
Vehicle Path

This paper discusses a method to determine the operation route for unmanned aerial vehicles for maritime surveillance. It is well known that there are several methods to make an aircraft path planning for ground related missions. On the other hand, path planning for maritime purposes is unnoticeable. The major problem of path planning for maritime is the abundant number of nodes which can make the route becomes quite long. Hence, reducing the number of nodes is necessary to rectify this problem. The main method is to separate the surveillance area into a smaller area of operation using clustering methods and then analyze the vulnerable area using the database to create an optimum flight path in each operation area. Although this paper specifically addresses a maritime-related mission, the path planning procedures can be applied to other missions as well. In this research, the input is given from satellite recorded data. Natuna Sea is chosen as the main discussion as the Natuna Sea currently is one of the most vulnerable regions in Indonesia for illegal fishing activity. The result shows that the aircraft path able to cover most of the vulnerable areas while optimizing the route distance.

scholarly journals Quantum Wind Driven Optimization for Unmanned Combat Air Vehicle Path Planning

2015 ◽  
Vol 5 (4) ◽  
pp. 1457-1483 ◽  
Author(s):  
Yongquan Zhou ◽  
Zongfan Bao ◽  
Rui Wang ◽  
Shilei Qiao ◽  
Yuxiang Zhou
Keyword(s):  
Path Planning ◽  
Air Vehicle ◽  
Vehicle Path

Micro air vehicle path planning in fuzzy quadtree framework

2011 ◽  
Vol 11 (8) ◽  
pp. 4859-4865 ◽  
Author(s):  
Sayan Ghosh ◽  
Abhishek Halder ◽  
Manoranjan Sinha
Keyword(s):  
Path Planning ◽  
Micro Air Vehicle ◽  
Air Vehicle ◽  
Vehicle Path

scholarly journals A review on graph search algorithms for optimal energy efficient path planning for an unmanned air vehicle

2019 ◽  
Vol 15 (2) ◽  
pp. 743 ◽  
Author(s):  
Sanjoy Kumar Debnath ◽  
Rosli Omar ◽  
Nor Badariyah Abdul Latip ◽  
Shasha Shely ◽  
Elia Nadira ◽  
...  
Keyword(s):  
Path Planning ◽  
Energy Efficient ◽  
Search Algorithms ◽  
Graph Search ◽  
Combinatorial Method ◽  
Optimal Energy ◽  
Unmanned Air Vehicle ◽  
Air Vehicle ◽  
Graph Search Algorithms

<span>Unmanned Air Vehicle (UAV) has attracted attention in recent years in conducting missions for longer time with higher levels of autonomy. For the enhanced autonomous characteristic of UAV, path planning is one of the crucial issues. Current researches on the graph search algorithms under combinatorial method are mainly reviewed in this paper by keeping focus on the comprehensive surveys of its properties for path planning. The outcome is a pen picture of their assumptions and drawbacks.</span>

Sampling-Based Path Planning for a Visual Reconnaissance Unmanned Air Vehicle

2012 ◽  
Vol 35 (2) ◽  
pp. 619-631 ◽  
Author(s):  
Karl J. Obermeyer ◽  
Paul Oberlin ◽  
Swaroop Darbha
Keyword(s):  
Path Planning ◽  
Unmanned Air Vehicle ◽  
Air Vehicle

scholarly journals A Novel Teaching-Learning-Based Optimization with Error Correction and Cauchy Distribution for Path Planning of Unmanned Air Vehicle

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Zhibo Zhai ◽  
Guoping Jia ◽  
Kai Wang
Keyword(s):  
Path Planning ◽  
Error Correction ◽  
Heuristic Method ◽  
Cauchy Distribution ◽  
Unmanned Air Vehicle ◽  
Tlbo Algorithm ◽  
Air Vehicle ◽  
Teaching Learning Based Optimization ◽  
Aerial Vehicle ◽  
Teaching Learning

Teaching-learning-based optimization (TLBO) algorithm is a novel heuristic method which simulates the teaching-learning phenomenon of a classroom. However, in the later period of evolution of the TLBO algorithm, the lower exploitation ability and the smaller scope of solutions led to the poor results. To address this issue, this paper proposes a novel version of TLBO that is augmented with error correction strategy and Cauchy distribution (ECTLBO) in which Cauchy distribution is utilized to expand the searching space and error correction to avoid detours to achieve more accurate solutions. The experimental results verify that the ECTLBO algorithm has overall better performance than various versions of TLBO and is very competitive with respect to other nine original intelligence optimization algorithms. Finally, the ECTLBO algorithm is also applied to path planning of unmanned aerial vehicle (UAV), and the promising results show the applicability of the ECTLBO algorithm for problem-solving.

MINIMIZING RADAR EXPOSURE IN AIR VEHICLE PATH PLANNING

2002 ◽  
Vol 35 (1) ◽  
pp. 181-185
Author(s):  
Meir Pachter ◽  
Jeffrey Hebert
Keyword(s):  
Path Planning ◽  
Air Vehicle ◽  
Vehicle Path
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