Collision Avoidance Planning Method of USV Based on Improved Ant Colony Optimization Algorithm

Wang, Hongjian; Guo, Feng; Yao, Hongfei; He, Shanshan; Xu, Xin

doi:10.1109/access.2019.2907783

Cited by 71 publications

(30 citation statements)

References 27 publications

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“…When a ship was detected, the range of its position during a given time frame was estimated, and the path planning system, considering COLREGs, produced a new, safe, and smooth path in real time [16]. Based on the motion velocity model and COLREGs, a reverse eccentric expansion method was designed to deal with dynamic obstacles [17]. A multiobjective optimization approach for path planning of an autonomous surface vehicle combined COLREGs with good seaman's practice and stratified the objectives [18].…”

Section: Introductionmentioning

confidence: 99%

“…(1) Based on the model proposed in [7,17], a USV collision avoidance model is established by the VO method considering the constraints of USV kinematic model, the rules of COLREGs, and the uncertainty of obstacle's velocity. e collision avoidance model not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…As a platform that can autonomously run in the ocean, lakes and rivers, USVs (Unmanned Surface Vehicles) can perform various civilian and military works, such as marine surveying and mapping, environmental monitoring, maritime search/rescue and military strikes [1][2][3]. Indeed, USVs have become popular in recent years, with researchers focusing on many related research areas, including collision avoidance [4][5][6], path planning [7,8] and navigation motion control [9,10], etc.…”

Section: Introductionmentioning

confidence: 99%

“…When the NSD of the overtaking vehicle is being violated, the overtaking USV shall keep out of the way for the vehicle being overtaken, as shown in Figure 1d. (4) Overtaken: A USV shall be deemed to be overtaken when coming up by another vehicle from a bearing of more than 22.5° abaft the beam. When the NSD of the overtaken vehicle is being violated, the overtaking USV shall keep out of the way of the vehicle being overtaken, the overtaken USV shall keep its course and speed, as shown in Figure 1e.…”

Section: Introductionmentioning

confidence: 99%

A COLREGs-Based Dynamic Navigation Safety Domain for Unmanned Surface Vehicles: A Case Study of Dolphin-I

Zhou

Wang

Zhang

2020

JMSE

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Unmanned Surface Vehicles (USVs) are intelligent machines that have been widely studied in recent years. The safety of USVs’ activities is a priority issue in their applications; one effective method is to delimit an exclusive safety domain around the USV. Besides considering collision avoidance, the safety domain should satisfy the requirements of encounter situations in the COLREGs (International Regulations for Preventing Collisions at Sea) as well. Whereas the model providing the safety domain for the USVs is defined through the experience of the manned ships, a specific model for USVs has been rarely studied. A dynamic navigation safety domain (DNSD) for USVs was proposed in this paper. To construct the model, the essential factors that could affect the navigation safety of the USVs were extracted via a rough set, and the extension functions of these factors were carried out. The DNSD was employed in various situations and compared with the ship domain models of common ships. It was found that the domain boundary can be automatically corrected according to the change in the working conditions when the DNSD is in use. Compared with the Fujii and Coldwell models, the DNSD can provide a larger safety area for a USV’s action of collision avoidance.

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“…To reduce calculation time, many heuristic methods represented by evolutionary computation have been proposed. Evolutionary computation is a kind of adaptive optimization probabilistic search algorithm, mainly including GA [6]- [8], ACO [9]- [12], improved ACO [12]- [15], particle swarm optimization [16], [17], improved particle swarm optimization [18]- [20], and artificial bee colony [21], [22] etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Path Planning Algorithm for Warehouse Robots Based on a Two-Dimensional Grid Model

Yang

Wang

et al. 2020

IEEE Access

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In this paper, the path planning problem of goods transportation is formulated as a traveling salesman problem (TSP). A novel path planning algorithm for warehouse robots based on a two-dimensional (2D) grid model is proposed to solve this type of TSP. Firstly, we simplified the traditional pile type warehouse as a node-based 2D grid model. Then, a new concept called the largest convex polygon (LCP) is introduced to illustrate the shortest path to traverse all goods locations in an ideal condition. Next, the remaining locations are classified by their relationship with LCP and designed path planning rules separately. Finally, we merged the paths of different types of cargo locations to get the final path. The experimental results show that, compared with ant colony optimization (ACO) and genetic algorithm (GA), our proposed algorithm could effectively reduce the computation time and total path length. INDEX TERMS Warehouse robots, path planning, TSP, 2D grid model, largest convex polygon.

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Collision Avoidance Planning Method of USV Based on Improved Ant Colony Optimization Algorithm

Cited by 71 publications

References 27 publications

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

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

A COLREGs-Based Dynamic Navigation Safety Domain for Unmanned Surface Vehicles: A Case Study of Dolphin-I

A Novel Path Planning Algorithm for Warehouse Robots Based on a Two-Dimensional Grid Model

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