Efficient Path Planning Method of USV for Intelligent Target Search

AbstractIn 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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Research on Local Dynamic Path Planning Method for Intelligent Vehicle Lane-Changing

Journal of Advanced Transportation ◽

10.1155/2019/4762658 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Zhi-qiang Liu ◽

Teng Zhang ◽

Yi-fan Wang

Keyword(s):

Path Planning ◽

Real Time ◽

Planning Method ◽

Local Dynamic ◽

Vehicle Speed ◽

Lane Changing ◽

Dynamic Path Planning ◽

Dynamic Path ◽

Real Time Information ◽

Time Information

A local dynamic path planning method is proposed to compensate for the lack of consideration of the movement state of surrounding vehicles, the poor comfort, and the low traffic efficiency when the existing vehicle changes lanes automatically. Firstly, the cubic polynomial is predefined, and the optimal track path is solved. According to the real-time information of environment perception, the model is continuously modified by acquiring real-time information in the course of path planning, and the regional safety of the vehicle is realized. The Carsim and simulink simulation results and actual vehicle verification show that, compared with the traditional nondynamic research method, this method can effectively solve the problem that the vehicle speed variation and the sudden intrusions of the vehicle leading to the compulsory operation of the vehicle during the course of lane-changing. The safety is also improved. In order to ensure the vehicle comfort and stability, the lane-changing time is shortened by 20%, and the efficiency of lane-changing is improved obviously.

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Intelligent Optimization Algorithm-Based Path Planning for a Mobile Robot

Computational Intelligence and Neuroscience ◽

10.1155/2021/8025730 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Qisong Song ◽

Shaobo Li ◽

Jing Yang ◽

Qiang Bai ◽

Jianjun Hu ◽

...

Keyword(s):

Path Planning ◽

Real Time ◽

Obstacle Avoidance ◽

Path Length ◽

Decision Model ◽

Ant Colony ◽

Global Path Planning ◽

Local Path Planning ◽

Local Path ◽

Safe Path

The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

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2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots

Journal of Advanced Transportation ◽

10.1155/2020/8867937 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Xuexi Zhang ◽

Jiajun Lai ◽

Dongliang Xu ◽

Huaijun Li ◽

Minyue Fu

Keyword(s):

Path Planning ◽

Mobile Robots ◽

Real Time ◽

Obstacle Avoidance ◽

Basic System ◽

Rescue Robot ◽

Planning Algorithm ◽

Local Path ◽

Slam Algorithm ◽

Path Planning Algorithm

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.

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A real-time local path planning method in complex scenes

10.1117/12.2602892 ◽

2021 ◽

Author(s):

Cai Wenlin ◽

Zihui Zhu ◽

Jianhua Li ◽

Jiaqi Liu ◽

Chunxi Wang

Keyword(s):

Path Planning ◽

Real Time ◽

Planning Method ◽

Local Path Planning ◽

Complex Scenes ◽

Local Path

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A novel method of unmanned surface vehicle autonomous cruise

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-05-2015-0097 ◽

2016 ◽

Vol 43 (1) ◽

pp. 121-130 ◽

Cited By ~ 7

Author(s):

Shaorong Xie ◽

Peng Wu ◽

Hengli Liu ◽

Peng Yan ◽

Xiaomao Li ◽

...

Keyword(s):

Path Planning ◽

Obstacle Detection ◽

Planning Method ◽

Dijkstra Algorithm ◽

Complex Environment ◽

Unmanned Surface Vehicle ◽

Content Type ◽

Global Path Planning ◽

Local Path Planning ◽

Local Path

Purpose – This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning. Design/methodology/approach – A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment. Findings – The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment. Originality/value – This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

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Research on Global Dynamic Path Planning Method Based on Improved A ∗ Algorithm

Mathematical Problems in Engineering ◽

10.1155/2021/4977041 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Chuanhu Niu ◽

Aijuan Li ◽

Xin Huang ◽

Wei Li ◽

Chuanyan Xu

Keyword(s):

Path Planning ◽

Real Time ◽

Obstacle Avoidance ◽

Planning Method ◽

Intelligent Vehicle ◽

Global Dynamic ◽

Dynamic Path Planning ◽

Dynamic Obstacle Avoidance ◽

Dynamic Path ◽

Dynamic Obstacle

Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved A ∗ algorithm. First, this method improves the heuristic function of the traditional A ∗ algorithm to improve the efficiency of global path planning. Second, this method uses a path optimization strategy to make the global path smoother. Third, this method is combined with the dynamic window method to improve the real-time performance of the dynamic obstacle avoidance of the intelligent vehicle. Finally, the global dynamic path planning method of the proposed improved A ∗ algorithm is verified through simulation experiments and real vehicle tests. In the simulation analysis, compared with the modified A ∗ algorithm and the traditional A ∗ algorithm, the method in this paper shortens the path distance by 2.5%∼3.0%, increases the efficiency by 10.3%∼13.6% and generates a smoother path. In the actual vehicle test, the vehicle can avoid dynamic obstacles in real time. Therefore, the method proposed in this paper can be applied on the intelligent vehicle platform. The path planning efficiency is high, and the dynamic obstacle avoidance is good in real time.

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