Unmanned aerial vehicle path planning based on A algorithm and its variants in 3d environment

Unmanned aerial vehicle path planning based on A algorithm and its variants in 3d environment

Finding a safe and optimum path from the source node to the target node, while preventing collisions with environmental obstacles, is always a challenging task. This task becomes even more complicated when the application area includes Unmanned Aerial Vehicle (UAV). This is because UAV follows an ae...

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Bibliographic Details
Published in:International journal of system assurance engineering and management 2021-10, Vol.12 (5), p.990-1000
Main Authors: Mandloi, Dilip, Arya, Rajeev, Verma, Ajit K.
Format: Article
Language:eng
Subjects:
Algorithms
Barriers
Computational efficiency
Computing time
Engineering
Engineering Economics
Logistics
Marketing
Nodes
Optimization
Organization
Original Article
Path planning
Quality Control
Reliability
Safety and Risk
Shortest-path problems
Time measurement
Unmanned aerial vehicles
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Summary:Finding a safe and optimum path from the source node to the target node, while preventing collisions with environmental obstacles, is always a challenging task. This task becomes even more complicated when the application area includes Unmanned Aerial Vehicle (UAV). This is because UAV follows an aerial path to reach the target node from the source node and the aerial paths are defined in 3D space. A* (A-star) algorithm is the path planning strategy of choice to solve path planning problem in such scenarios because of its simplicity in implementation and promise of optimality. However, A* algorithm guarantees to find the shortest path on graphs but does not guarantee to find the shortest path in a real continuous environment. Theta* (Theta-star) and Lazy Theta* (Lazy Theta-star) algorithms are variants of the A* algorithm that can overcome this shortcoming of the A* algorithm at the cost of an increase in computational time. In this research work, a comparative analysis of A-star, Theta-star, and Lazy Theta-star path planning strategies is presented in a 3D environment. The ability of these algorithms is tested in 2D and 3D scenarios with distinct dimensions and obstacle complexity. To present comparative performance analysis of considered algorithms two performance metrices are used namely computational time which is a measure of time taken to generate the path and path length which represents the length of the generated path.
ISSN:0975-6809
0976-4348