Obstacle Avoidance Path Planning Using the Elite Ant Colony Algorithm for Parameter Optimization of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have attracted considerable research attention because of their strong interoperability, high flexibility, and excellent maneuverability. Path planning and autonomous obstacle avoidance are critical for UAVs. In this study, multiobjective optimization using the ant co...

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Bibliographic Details
Published in:Arabian journal for science and engineering (2011) Vol. 48; no. 2; pp. 2261 - 2275
Main Authors: Meng, Xiaoling, Zhu, Xijing, Zhao, Jing
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Springer Nature B.V
Subjects:
Algorithms
Ant colony optimization
Engineering
Humanities and Social Sciences
Maneuverability
multidisciplinary
Multiple objective analysis
Obstacle avoidance
Parameters
Path planning
Prediction models
Regression models
Reliability analysis
Research Article-Computer Engineering and Computer Science
Response surface methodology
Science
Statistical analysis
Unmanned aerial vehicles
Variance analysis
Regression prediction model
Unmanned aerial vehicle (UAV) path planning
Parameter optimization
Elite ant colony algorithm
Analysis of variance
ISSN:2193-567X, 1319-8025, 2191-4281
Online Access:Get full text
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Summary:Unmanned aerial vehicles (UAVs) have attracted considerable research attention because of their strong interoperability, high flexibility, and excellent maneuverability. Path planning and autonomous obstacle avoidance are critical for UAVs. In this study, multiobjective optimization using the ant colony algorithm was performed for solving the UAV obstacle avoidance path planning problem. To overcome the easy-to-fall-into-deadlock tendency and slow convergence speed of the conventional ant colony algorithm, an elite ant colony algorithm was proposed for improving path selection probability and pheromone update strategy. Next, the response surface method was used to analyze the key parameters in the improved algorithm, construct the regression prediction model of response indicators, perform variance analysis, and verify the reliability of the model. The key parameters were optimized to obtain the best parameter combination, and simulation experiments were conducted. The results revealed that the performance of path length, running time, and robustness in various terrains improved considerably. Thus the proposed method is a feasible scheme for the path planning of UAVs in military search and rescue and material transportation.
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-022-07204-7