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Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization
Joint Authors
Han, Zhiwei
Zhao, Bo
Wang, Xinwei
Xia, Guoqing
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-13
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
An unmanned surface vehicle (USV) plans its global path before the mission starts.
When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision.
This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance.
The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle.
According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision.
Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA).
In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space.
The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV.
Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value.
MQPSO is primarily proposed to meet that requirement.
In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined.
Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.
American Psychological Association (APA)
Xia, Guoqing& Han, Zhiwei& Zhao, Bo& Wang, Xinwei. 2020. Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization. Complexity،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1141286
Modern Language Association (MLA)
Xia, Guoqing…[et al.]. Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization. Complexity No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1141286
American Medical Association (AMA)
Xia, Guoqing& Han, Zhiwei& Zhao, Bo& Wang, Xinwei. Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization. Complexity. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1141286
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141286