scholarly journals A Method of Path Planning on Safe Depth for Unmanned Surface Vehicles Based on Hydrodynamic Analysis

2019 ◽  
Vol 9 (16) ◽  
pp. 3228 ◽  
Author(s):  
Liu ◽  
Wang ◽  
Zhang
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Risk Level ◽  
Hydrodynamic Characteristics ◽  
Shallow Waters ◽  
A Algorithm ◽  
Hydrodynamic Analysis ◽  
Unmanned Surface Vehicles ◽  
Safe Navigation ◽  
Navigation Errors

The depth of water is of great significance to the safe navigation of unmanned surface vehicles (USV)in shallow waters, such as islands and reefs. How to consider the influence of depth on the safety of USV navigation and path planning is relatively rare. Under the condition of ocean disturbance, the hydrodynamic characteristics of unmanned surface vehicles will affect its draft and depth safety. In this paper, the hydrodynamic model of unmanned surface vehicles is analyzed, and a water depth risk level A* algorithm (WDRLA*) is proposed. According to the depth point of the electronic navigation chart (ENC), the gridding depth can be obtained by spline function interpolation. The WDRLA* algorithm is applied to plan the path, which takes hydrodynamic characteristics and navigation errors into account. It is compared with the traditional A* shortest path and safest path. The simulation results show that the WDRLA* algorithm can reduce the depth hazard of the shortest path and ensure the safety of navigation.

scholarly journals An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles

2020 ◽  
Vol 73 (6) ◽  
pp. 1247-1262
Author(s):  
Yang Long ◽  
Zheming Zuo ◽  
Yixin Su ◽  
Jie Li ◽  
Huajun Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Path Planning ◽  
Optimal Parameter ◽  
A Algorithm ◽  
Unmanned Surface Vehicles ◽  
Parameter Combination ◽  
Bacterial Foraging ◽  
Optimisation Algorithm ◽  
Global Path Planning ◽  
Influential Parameters

The bacterial foraging optimisation (BFO) algorithm is a commonly adopted bio-inspired optimisation algorithm. However, BFO is not a proper choice in coping with continuous global path planning in the context of unmanned surface vehicles (USVs). In this paper, a grid partition-based BFO algorithm, named AS-BFO, is proposed to address this issue in which the enhancement is contributed by the involvement of the A* algorithm. The chemotaxis operation is redesigned in AS-BFO. Through repeated simulations, the relative optimal parameter combination of the proposed algorithm is obtained and the most influential parameters are identified by sensitivity analysis. The performance of AS-BFO is evaluated via five size grid maps and the results show that AS-BFO has advantages in USV global path planning.

scholarly journals Path Planning of Mobile Robot in Unknown Environment

2010 ◽  
pp. 122-127
Author(s):  
Pradipta kumar Das ◽  
Romesh Laishram ◽  
Amit Konar
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cost Function ◽  
Shortest Path ◽  
Target Position ◽  
Initial Position ◽  
A Algorithm ◽  
Unknown Environment ◽  
Feasible Path ◽  
The Cost

In this paper, we study the online path planning for khepera II mobile robot in an unknown environment. The well known heuristic A* algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance and time metric as the cost function.

scholarly journals Global Dynamic Path Planning Fusion Algorithm Combining Jump-A* Algorithm and Dynamic Window Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 19632-19638
Author(s):  
Lisang Liu ◽  
Jinxin Yao ◽  
Dongwei He ◽  
Jian Chen ◽  
Jing Huang ◽  
...  
Keyword(s):  
Path Planning ◽  
A Algorithm ◽  
Fusion Algorithm ◽  
Global Dynamic ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Window Approach

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

scholarly journals 3D Path Planning of UAV Based on Improved A * Algorithm

2017 ◽  
Vol 12 ◽  
pp. 01015
Author(s):  
Zhe-Tong Tian ◽  
Yan Ding ◽  
Jian-Mei Song ◽  
Liang-Jin Zhao ◽  
Yu-Tong Zhang
Keyword(s):  
Path Planning ◽  
A Algorithm
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