Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

Xia, Guoqing; Han, Zhiwei; Zhao, Bo; Liu, Caiyun; Wang, Xinwei

doi:10.1155/2019/2902170

Cited by 46 publications

(23 citation statements)

References 24 publications

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“…Environmental disturbances: Table 3 shows that when it comes to environmental disturbances, more than half (27, or 60%) of the papers do not take any environmental disturbances into consideration. Several papers [e.g., 25,26,27] are focusing only on the effect of current on the vessel (7, or 16%), some consider both current and wind (4, or 9%) [47,49,53,54], and only two papers consider both current, wind, and waves [29,35]. None of the papers consider waves as the only environmental disturbance affecting the ship's movement; however, waves are included in two papers together with wind and current.…”

Section: Properties Of Algorithmsmentioning

confidence: 99%

“…The algorithm is able to avoid collisions in severely congested and restricted waters The algorithm needs to be enhanced more for realistic applications It does not consider the change of speed as an action for collision avoidance Deep reinforcement learning [45] The approach has an excellent adaptability to unknown complex environments with various encountered ships High sample complexity, difficult to use in learning in the real world Improved quantum ACO [29] The algorithm can plan a path considering multiple objectives simultaneously The number of iterations required to converge to the minimum was 11.2-24.5% lower than for the quantum ACO and ACO…”

Section: Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

et al. 2021

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Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios.

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Section: Properties Of Algorithmsmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

et al. 2021

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“…Hence, the local path planning for USV collision avoidance is transformed to a multiobjective optimization problem. e energy consumption while sailing determines the USV's endurance and duration [34].…”

Section: Optimization Model Of Local Pathmentioning

confidence: 99%

“…Based on the QEA with a quantum rotation gate strategy, an adaptive evolution-based quantum-inspired evolutionary algorithm (AEQEA) introduced an adaptive evolution mechanism [32]. A quantum ant colony algorithm was used to plan the global path of a USV [33,34]. A quantum evolutionary algorithm was integrated into particle swarm optimization [35].…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

et al. 2020

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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.

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“…The global path planning of USV is a large-scale and longendurance path planning, which can be defined as based on the prior information provided by electronic charts, comprehensive consideration of operational tasks and their own navigation characteristics plan a non-touch path from the starting point to the target (Xia et al 2019).…”

Section: Global Path Planning Based On Prior Informationmentioning

confidence: 99%

Efficient Path Planning Method of USV for Intelligent Target Search

Zhang

Liu

et al. 2019

J geovis spat anal

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In order to accomplish a target search task safely and efficiently and make full use of prior information and real-time information, a path planning method of unmanned surface vehicle (USV) for intelligent target search is proposed. The overall strategy is divided into three parts: global path planning based on prior information, local path planning based on real-time information, and improved A* obstacle avoidance algorithm. Before the start of the task, the global path planning is carried out based on prior information such as the initial position of USV, the predicted position of the target and range of search area. After the start of the task, if USV finds suspicious targets, in order to further approach these suspicious targets, it will enter different local path planning modes according to the characteristics of these targets. During task execution, if obstacles are encountered, an improved A* obstacle avoidance algorithm is adopted. The simulation results show that the proposed method can improve the efficiency of target recognition and reduce the turning cost of USV when encountering obstacles. So, for USV intelligent target search, the proposed path planning method can save resources and improve search efficiency.

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Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

Cited by 46 publications

References 24 publications

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

Efficient Path Planning Method of USV for Intelligent Target Search

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