Formation Path Planning for Collaborative Autonomous Underwater Vehicles Based on Consensus-Sparrow Search Algorithm

Formation path planning of autonomous underwater vehicles (AUVs) entails establishing optimal collision-free routes over challenging underwater terrain while maintaining state coherence to preserve an intended formation, and path planning techniques have been the subject of significant study over th...

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Vydáno v:IEEE internet of things journal Ročník 11; číslo 8; s. 13810 - 13823
Hlavní autoři: Zhang, Jie, Chen, Dugui, Han, Guangjie, Qian, Yujie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 15.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Autonomous underwater vehicle (AUV)
Autonomous underwater vehicles
Collaboration
Collision avoidance
Consensus algorithm
Control methods
Formation control
Obstacle avoidance
Optimization
Particle swarm optimization
Path planning
Potential fields
Search algorithms
sparrow search algorithm (SSA)
Swarm intelligence
ISSN:2327-4662, 2327-4662
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Shrnutí:Formation path planning of autonomous underwater vehicles (AUVs) entails establishing optimal collision-free routes over challenging underwater terrain while maintaining state coherence to preserve an intended formation, and path planning techniques have been the subject of significant study over the last decade, with swarm intelligence algorithms, such as the sparrow search algorithm (SSA), being among the most commonly employed. However, the algorithms typically are constrained by the imbalanced adjustment between local development and global exploration, which reduces the optimization capability, and they are relatively understudied for the formation movement issues. Accordingly, this article proposes a consensus-SSA-based formation path planning (CSFPP) method, which applies an improved SSA for planning an optimal path, and then incorporates the path into a consensus algorithm that introduces an artificial potential field (APF) to enable collaborative formation movement. In the path planning phrase, the CSFPP employs an improved SSA which applies the golden search optimization (GSO) and an adaptive iteration approach to adjust the local development and global exploration in order to improve the overall optimization performance. Then in the formation control phrase, the CSFPP introduces a virtual point scheme for APF-based obstacle avoidance in order to navigate an AUV formation in an obstacle environment while maintaining the formation shape controlled by a consensus algorithm. The superiority of the proposed path planning capability is demonstrated by comparing the convergence performance of the improved SSA with the recent contributions; and simulations of formation movement in underwater space verify the feasibility of the proposed formation control method in the obstacle environment.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3340432