scholarly journals Rapid global path planning algorithm for unmanned surface vehicles in large-scale and multi-island marine environments

2021 ◽  
Vol 7 ◽  
pp. e612
Author(s):  
Dong Wang ◽  
Jie Zhang ◽  
Jiucai Jin ◽  
Deqing Liu ◽  
Xingpeng Mao
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Distance Constraint ◽  
Marine Environments ◽  
Fast Marching ◽  
Unmanned Surface Vehicles ◽  
Grid Map ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

A global path planning algorithm for unmanned surface vehicles (USVs) with short time requirements in large-scale and complex multi-island marine environments is proposed. The fast marching method-based path planning for USVs is performed on grid maps, resulting in a decrease in computer efficiency for larger maps. This can be mitigated by improving the algorithm process. In the proposed algorithm, path planning is performed twice in maps with different spatial resolution (SR) grids. The first path planning is performed in a low SR grid map to determine effective regions, and the second is executed in a high SR grid map to rapidly acquire the final high precision global path. In each path planning process, a modified inshore-distance-constraint fast marching square (IDC-FM2) method is applied. Based on this method, the path portions around an obstacle can be constrained within a region determined by two inshore-distance parameters. The path planning results show that the proposed algorithm can generate smooth and safe global paths wherein the portions that bypass obstacles can be flexibly modified. Compared with the path planning based on the IDC-FM2 method applied to a single grid map, this algorithm can significantly improve the calculation efficiency while maintaining the precision of the planned path.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Guoqing Xia ◽  
Zhiwei Han ◽  
Bo Zhao ◽  
Caiyun Liu ◽  
Xinwei Wang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
High Efficiency ◽  
Multiple Objectives ◽  
Ant Colony ◽  
Marine Environments ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Number Of Iterations

As a tool to monitor marine environments and to perform dangerous tasks instead of manned vessels, unmanned surface vehicles (USVs) have extensive applications. Because most path planning algorithms have difficulty meeting the mission requirements of USVs, the purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments. A global path planning algorithm based on an improved quantum ant colony algorithm (IQACA) is proposed. The improved quantum ant colony algorithm is an algorithm that benefits from the high efficiency of quantum computing and the optimization ability of the ant colony algorithm. The proposed algorithm can plan a path considering multiple objectives simultaneously. The simulation results show that the proposed algorithm’s obtained minimum was 2.1–6.5% lower than those of the quantum ant colony algorithm (QACA) and ant colony algorithm (ACA), and the number of iterations required to converge to the minimum was 11.2–24.5% lower than those of the QACA and ACA. In addition, the optimized path for the USV was obtained effectively and efficiently.

scholarly journals Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles

2021 ◽  
Vol 11 (16) ◽  
pp. 7378
Author(s):  
Hongchao Zhuang ◽  
Kailun Dong ◽  
Yuming Qi ◽  
Ning Wang ◽  
Lei Dong
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Working Conditions ◽  
Planning Method ◽  
Grid Map ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Global Planning ◽  
Path Planning Algorithm

In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of mobile robots. Based on the rules of the live chess game in Go, the grid map is optimized and reconstructed. This grid of environment and the obstacle values of grid environment between each two destination points are obtained. Using the simulated annealing strategy, the optimization of multi-destination arrival sequence for the mobile robot is implemented by combining with the obstacle value between two destination points. The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path. The optimal path of multiple destinations is acquired when mobile robots can pass through the fewest obstacles. The multi-destination path planning simulation of the mobile robot is implemented by MATLAB software (Natick, MA, USA, R2016b) under multiple working conditions. The Pareto numerical graph is obtained. According to comparing multi-destination global planning with single-destination path planning under the multiple working conditions, the length of path in multi-destination global planning is reduced by 22% compared with the average length of the single-destination path planning algorithm. The results show that the multi-destination global path planning method of the mobile robot based on the optimal obstacle value is reasonable and effective. Multi-destination path planning method proposed in this article is conducive to improve the terrain adaptability of mobile robots.

scholarly journals An energy-efficient path planning algorithm for unmanned surface vehicles

2018 ◽  
Vol 161 ◽  
pp. 308-321 ◽  
Author(s):  
Hanlin Niu ◽  
Yu Lu ◽  
Al Savvaris ◽  
Antonios Tsourdos
Keyword(s):  
Path Planning ◽  
Energy Efficient ◽  
Unmanned Surface Vehicles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals A Path-Planning Strategy for Unmanned Surface Vehicles Based on an Adaptive Hybrid Dynamic Stepsize and Target Attractive Force-RRT Algorithm

2019 ◽  
Vol 7 (5) ◽  
pp. 132 ◽  
Author(s):  
Zhen Zhang ◽  
Defeng Wu ◽  
Jiadong Gu ◽  
Fusheng Li
Keyword(s):  
Path Planning ◽  
High Speed ◽  
Attractive Force ◽  
Simulation Experiments ◽  
Planning Strategy ◽  
Unmanned Surface Vehicles ◽  
Planning Algorithm ◽  
Pass Through ◽  
Path Planning Algorithm ◽  
Narrow Area

It is well known that path planning has always been an important study area for intelligent ships, especially for unmanned surface vehicles (USVs). Therefore, it is necessary to study the path-planning algorithm for USVs. As one of the basic algorithms for USV path planning, the rapidly-exploring random tree (RRT) is popular due to its simple structure, high speed and ease of modification. However, it also has some obvious drawbacks and problems. Designed to perfect defects of the basic RRT and improve the performance of USVs, an enhanced algorithm of path planning is proposed in this study, called the adaptive hybrid dynamic stepsize and target attractive force-RRT(AHDSTAF-RRT). The ability to pass through a narrow area and the forward speed in open areas of USVs are improved by adopting the AHDSTAF-RRT in comparison to the basic RRT algorithm. The improved algorithm is also applied to an actual gulf map for simulation experiments, and the experimental data is collected and organized. Simulation experiments show that the proposed AHDSTAF-RRT in this paper outperforms several existing RRT algorithms, both in terms of path length and calculating speed.

A Global Path Planning Algorithm for AGV Based on the Shapefile Vectorgraph

2014 ◽  
Vol 889-890 ◽  
pp. 1117-1120
Author(s):  
Li Fei Song ◽  
Ming Rui Tang ◽  
Nan Li
Keyword(s):  
Path Planning ◽  
Planning Period ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

The paper presents a global path planning algorithm for AGV based on shapefile vectorgrath in order to meet the requirements of future manufacturing factory for automation and intelligentialize.The algorithm could distinguish different barriers but not just recognize there is a barrier or not. and the adoption of shapefile vectorgrath makes it possible not to rasterize the map or simplify the obstacles during the global path planning period. Simulation result shows the feasibility of the algorithm.

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