Implementation of Multiple UAV (Unmanned Aerial Vehicle) Algorithm for same Mission profile

To find out the path in uncertain environment is very difficult for flying machine without a pilot. Sometimes the evaluation process takes a lot of time and swarm of drones experiences delays in command execution in real time. With the use of intelligent swarm algorithms like RRT and A*, even swarm of UAVs have limited capability. In this paper, RRT (Rapidly Exploring Random tree) and A* have been implemented for the same mission profile with same capability of UAV in different environments and obstacles. This paper also illustrates the use of different attributes to examine the optimization result of RRT and A* Algorithm.

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Publication Details

Published
2024-11-23
DOI
https://doi.org/10.1109/icicat62666.2024.10923453
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.25
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article

Implementation of Multiple UAV (Unmanned Aerial Vehicle) Algorithm for same Mission profile

Mani Shankar Prasad, Vaibhav, Nour Mohammad Murad, Shivani Verma
1 citations
Robotic Path Planning Algorithms
0.25
article

Implementation of Multiple UAV (Unmanned Aerial Vehicle) Algorithm for same Mission profile

Mani Shankar Prasad, Vaibhav, Nour Mohammad Murad, Shivani Verma
article en
1 citations

Abstract

To find out the path in uncertain environment is very difficult for flying machine without a pilot. Sometimes the evaluation process takes a lot of time and swarm of drones experiences delays in command execution in real time. With the use of intelligent swarm algorithms like RRT and A*, even swarm of UAVs have limited capability. In this paper, RRT (Rapidly Exploring Random tree) and A* have been implemented for the same mission profile with same capability of UAV in different environments and obstacles. This paper also illustrates the use of different attributes to examine the optimization result of RRT and A* Algorithm.

Amity University (IN)
Openalex Percentile: Top 41%
Robotic Path Planning Algorithms
0.25
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