Paper Info
Title
A Novel Uav Path Planning Algorithm To Search For Floating Objects On The Ocean Surface Based On Object'S Trajectory Prediction By Regression
Abstract
Search and find mission in ocean environment is a none trivial operation given the amount of random parameters associated with it. The uncertain and dynamic aspects related to ocean current movement make the trajectory prediction of drifting lost object onto sea water a very complicated task. In this work we present a novel lost target searching algorithm based on Recursive Area Clustering and target trajectory predication in ocean environment. Based on the widely known GlobCurrent v2 dataset which model the drifting of ocean surface current using satellite sensory data combined with mathematical and simulation modeling, we propose a regression algorithm based on our Recursive Area Clustering algorithm that we have developed previously to determine the strategic zones (weight centers) characterizing the high density areas extracted from drifting target history. Given those weight centers, we predict the object trajectory through refined regression. The predicted lost object trajectory is used to plan the path of UAV search mission. The model developed has a significant impact as we have tested our strategy in a scenario for searching an area covering 68517 km(2), we have shown that 78% of the time, the lost object can be found within 32 km distance of the predicted trajectories limiting the significant search area to be about 5% of the whole searched area. (c) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.robot.2020.103673
ROBOTICS AND AUTONOMOUS SYSTEMS
Keywords
DocType
Volume
Dynamic target path prediction, UAV, High dense clustering, Surface Ekman current, Machine Leaning Regression
Journal
135
ISSN
Citations 
PageRank 
0921-8890
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Mehrez Boulares100.34
Ahmed Barnawi200.34