Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization
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 mod...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3095426 |
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| author | Guoqing Xia Zhiwei Han Bo Zhao Xinwei Wang |
| author_facet | Guoqing Xia Zhiwei Han Bo Zhao Xinwei Wang |
| author_sort | Guoqing Xia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a1896416cf954e8aa06624a15322db51 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a1896416cf954e8aa06624a15322db512025-02-03T06:46:24ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/30954263095426Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm OptimizationGuoqing Xia0Zhiwei Han1Bo Zhao2Xinwei Wang3College of Automation, Harbin Engineering University, Harbin 150001, ChinaCollege of Automation, Harbin Engineering University, Harbin 150001, ChinaCollege of Automation, Harbin Engineering University, Harbin 150001, ChinaCollege of Automation, Harbin Engineering University, Harbin 150001, ChinaAn 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.http://dx.doi.org/10.1155/2020/3095426 |
| spellingShingle | Guoqing Xia Zhiwei Han Bo Zhao Xinwei Wang Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization Complexity |
| title | Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization |
| title_full | Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization |
| title_fullStr | Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization |
| title_full_unstemmed | Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization |
| title_short | Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization |
| title_sort | local path planning for unmanned surface vehicle collision avoidance based on modified quantum particle swarm optimization |
| url | http://dx.doi.org/10.1155/2020/3095426 |
| work_keys_str_mv | AT guoqingxia localpathplanningforunmannedsurfacevehiclecollisionavoidancebasedonmodifiedquantumparticleswarmoptimization AT zhiweihan localpathplanningforunmannedsurfacevehiclecollisionavoidancebasedonmodifiedquantumparticleswarmoptimization AT bozhao localpathplanningforunmannedsurfacevehiclecollisionavoidancebasedonmodifiedquantumparticleswarmoptimization AT xinweiwang localpathplanningforunmannedsurfacevehiclecollisionavoidancebasedonmodifiedquantumparticleswarmoptimization |