scholarly journals Research on Ship Meteorological Route Based on A-Star Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ge Chen ◽  
Tao Wu ◽  
Zheng Zhou
Keyword(s):  
Real Time ◽  
Optimal Path ◽  
Route Planning ◽  
Planning Method ◽  
Target Point ◽  
Convex Shape ◽  
Weather Information ◽  
Starting Point ◽  
Motion Characteristics ◽  
Initial Route

Ship meteorological navigation is based on hydrometeorological data of a certain time scale, considering the ship’s motion characteristics and its own characteristics. First, we provide the best route for the ship and then use real-time local weather information to correct the route during the ship’s navigation. It can also be expressed as follows: it is based on the hydrological and meteorological conditions of the ship during its voyage and the seakeeping characteristics of the ship itself, and the route planning method is used to select the best route for the ship. The best route is a balance between economy and safety, that is, based on ensuring the safety of ship navigation, the route that meets the shortest navigation time, the least fuel consumption, or the least navigation risk is obtained. Weather navigation includes the optimization of the initial route before sailing and the correction of the route after sailing. As there may be errors in hydrometeorological forecasts, especially in the accuracy and real-time performance of medium and long-term forecasts, the optimal initial route may not achieve the best results. Therefore, after the ship sails, it is necessary to adjust and correct the preferred initial route based on the meteorological information detected by the sensors or the continuously updated hydrometeorological forecast data to ensure the best effect of meteorological navigation. This paper proposes a weather route planning method based on the improved A-star algorithm. The convex shape of the concave obstacle and the expansion of the obstacle are carried out; according to the position of the target point relative to the starting point, the search direction of the A-star algorithm at each node is restricted, and an improved A-star algorithm is proposed. The simulation of global weather route planning shows that the improved A-star algorithm can not only find the optimal path but also effectively reduce the number of nodes that the algorithm needs to search during operation. Compared with the classic algorithm, the improved algorithm reduces the number of node searches by 29.25%.

Two-phase A*: A real-time global motion planning method for non-holonomic unmanned ground vehicles

2020 ◽  
pp. 095440702094839
Author(s):  
Kai Zhang ◽  
Yi Yang ◽  
Mengyin Fu ◽  
Meiling Wang
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Optimal Path ◽  
Computation Time ◽  
Planning Method ◽  
Global Motion ◽  
Two Phase ◽  
Holonomic Constraints ◽  
Planning Cycle ◽  
Indoor And Outdoor Environments

This paper presents a search-based global motion planning method, called the two-phase A*, with an adaptive heuristic weight. This method is suitable for planning a global path in real time for a car-like vehicle in both indoor and outdoor environments. In each planning cycle, the method first estimates a proper heuristic weight based on the hardness of the planning query. Then, it finds a nearly optimal path subject to the non-holonomic constraints using an improved A* with a weighted heuristic function. By estimating the heuristic weight dynamically, the two-phase A* is able to adjust the optimality level of its path based on the hardness of the planning query. Therefore, the two-phase A* sacrifices little planning optimality, and its computation time is acceptable in most situations. The two-phase A* has been implemented and tested in the simulations and real-world experiments over various task environments. The results show that the two-phase A* can generate a nearly optimal global path dynamically, which satisfies the non-holonomic constraints of a car-like vehicle and reduces the total navigation time.

A real-time intelligent route planning method in geomagnetic navigation

2012 ◽  
Vol 2 (2) ◽  
pp. 125 ◽  
Author(s):  
Yuan Sun ◽  
Jinsheng Zhang ◽  
Shicheng Wang ◽  
Wei Jiao
Keyword(s):  
Real Time ◽  
Route Planning ◽  
Planning Method

scholarly journals Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm

2017 ◽  
Vol 12 (4) ◽  
pp. 26-35 ◽  
Author(s):  
Nizar Hadi Abbas ◽  
Farah Mahdi Ali
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Target Point ◽  
Autonomous Mobile Robot ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Starting Point ◽  
Bacterial Foraging Optimization Algorithm

This paper describes the problem of online autonomous mobile robot path planning, which is consisted of finding optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An enhanced algorithm for solving the problem of path planning using Bacterial Foraging Optimization algorithm is presented. This nature-inspired metaheuristic algorithm, which imitates the foraging behavior of E-coli bacteria, was used to find the optimal path from a starting point to a target point. The proposed algorithm was demonstrated by simulations in both static and dynamic different environments. A comparative study was evaluated between the developed algorithm and other two state-of-the-art algorithms. This study showed that the proposed method is effective and produces trajectories with satisfactory results.

A Real-Time Route Planning Method Based on DeepLabV3+ for Plant Protection UAVs

2021 ◽  
Author(s):  
Junjie Wan ◽  
Lijun Qi ◽  
Hao Zhang ◽  
Zhong�ao Lu ◽  
Jiarui Zhou
Keyword(s):  
Real Time ◽  
Plant Protection ◽  
Route Planning ◽  
Planning Method

scholarly journals Research on E-commerce Logistics Distribution Path Planning Based on Improved Genetic Algorithm

2022 ◽  
Vol 16 ◽  
pp. 202-208
Author(s):  
Lan Lan
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Rapid Development ◽  
Optimal Path ◽  
Route Planning ◽  
Planning Method ◽  
Improved Genetic Algorithm ◽  
Logistics Distribution ◽  
Centralized Planning ◽  
Distribution Efficiency

With the rapid development of the Internet, e-commerce business has gradually emerged. However, its logistics distribution route planning method has problems such as redundancy of logistics data, which cannot achieve centralized planning of distribution paths, resulting in low e-commerce logistics distribution efficiency and long distribution distances, higher cost. Therefore, in order to improve the ability of logistics distribution path planning, this paper designs an e-commerce logistics distribution path planning method based on improved genetic algorithm. Optimize the analysis of e-commerce logistics distribution nodes, establish a modern logistics distribution system, and optimize the total transportation time and transportation cost under the location model of the logistics distribution center. Using hybrid search algorithm and improved genetic algorithm parameters, an improved genetic algorithm distribution path planning model is established to select the optimal path of logistics distribution, and realize e-commerce logistics distribution path with high accuracy, low error and good convergence. planning. According to the experimental results, the method in this paper can effectively shorten the distance of e-commerce logistics distribution path, reduce the number of distribution vehicles, reduce distribution costs, improve distribution efficiency, and effectively achieve centralized planning of logistics distribution. Therefore, the e-commerce logistics distribution route planning method based on improved genetic algorithm has high practical application value.

scholarly journals Optimal Path Planning using Equilateral Spaces Oriented Visibility Graph Method

2017 ◽  
Vol 7 (6) ◽  
pp. 3046 ◽  
Author(s):  
Nor Badariyah Abdul Latip ◽  
Rosli Omar ◽  
Sanjoy Kumar Debnath
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Optimal Path ◽  
Computation Time ◽  
Visibility Graph ◽  
Target Point ◽  
Improvement Rate ◽  
Starting Point ◽  
Autonomous Cars ◽  
Planning Methods

<span>Path planning has been an important aspect in the development of autonomous cars in which path planning is used to find a collision-free path for the car to traverse from a starting point Sp to a target point Tp. The main criteria for a good path planning algorithm include the capability of producing the shortest path with a low computation time. Low computation time makes the autonomous car able to re-plan a new collision-free path to avoid accident. However, the main problem with most path planning methods is their computation time increases as the number of obstacles in the environment increases. In this paper, an algorithm based on visibility graph (VG) is proposed. In the proposed algorithm, which is called Equilateral Space Oriented Visibility Graph (ESOVG), the number of obstacles considered for path planning is reduced by introducing a space in which the obstacles lie. This means the obstacles located outside the space are ignored for path planning. From simulation, the proposed algorithm has an improvement rate of up to 90% when compared to VG. This makes the algorithm is suitable to be applied in real-time and will greatly accelerate the development of autonomous cars in the near future.</span>

Neural Network Based Control System for Robots Group Operating in 2-d Uncertain Environment

2020 ◽  
Vol 21 (8) ◽  
pp. 470-479
Author(s):  
A. R. Gaiduk ◽  
O. V. Martjanov ◽  
M. Yu. Medvedev ◽  
V. Kh. Pshikhopov ◽  
N. Hamdan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Path Planning ◽  
Shortest Path ◽  
Optimal Path ◽  
Type A ◽  
Planning Method ◽  
Target Point ◽  
Optimal Path Planning

This study is devoted to development of a neural network based control system of robots group. The control system performs estimation of an environment state, searching the optimal path planning method, path planning, and changing the trajectories on via the robots interaction. The deep learning neural networks implements the optimal path planning method, and path planning of the robots. The first neural network classifies the environment into two types. For the first type a method of the shortest path planning is used. For the second type a method of the most safety path planning is used. Estimation of the path planning algorithm is based on the multi-objective criteria. The criterion includes the time of movement to the target point, path length, and minimal distance from the robot to obstacles. A new hybrid learning algorithm of the neural network is proposed. The algorithm includes elements of both a supervised learning as well as an unsupervised learning. The second neural network plans the shortest path. The third neural network plans the most safety path. To train the second and third networks a supervised algorithm is developed. The second and third networks do not plan a whole path of the robot. The outputs of these neural networks are the direction of the robot’s movement in the step k. Thus the recalculation of the whole path of the robot is not performed every step in a dynamical environment. Likewise in this paper algorithm of the robots formation for unmapped obstructed environment is developed. The results of simulation and experiments are presented.

Sign in / Sign up

Export Citation Format

Share Document