Path Planning for Ground Simulation Objiect Based on A* Algorithm

2012 ◽  
Vol 229-231 ◽  
pp. 2019-2024 ◽  
Author(s):  
Zhi Qiang Zhao ◽  
Zhi Hua Liu ◽  
Jia Xin Hao
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Optimal Path ◽  
Planning Method ◽  
Heuristic Function ◽  
A Algorithm

In the process of ground simulation object maneuver simulation in large-scale operation simulation, an efficient path planning method based on A*algorithm is proposed. By means of introducing all kind of geography factors and security factors into heuristic function, the plan reaching method solves the problem of finding an optimal path under acquiring enemy's situation and terrain data. Experiment results show that it has effectively raised path planning speed of A* algorithm and the scheme is practical and feasible.

Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment

2021 ◽  
pp. 095441002110073
Author(s):  
Zhe Zhang ◽  
Jian Wu ◽  
Jiyang Dai ◽  
Cheng He
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Optimal Path ◽  
Radar System ◽  
Planning Method ◽  
Optimal Path Planning ◽  
Aerial Vehicles ◽  
3D Network ◽  
Planning Scheme ◽  
Estimation Function

For stealth unmanned aerial vehicles (UAVs), path security and search efficiency of penetration paths are the two most important factors in performing missions. This article investigates an optimal penetration path planning method that simultaneously considers the principles of kinematics, the dynamic radar cross-section of stealth UAVs, and the network radar system. By introducing the radar threat estimation function and a 3D bidirectional sector multilayer variable step search strategy into the conventional A-Star algorithm, a modified A-Star algorithm was proposed which aims to satisfy waypoint accuracy and the algorithm searching efficiency. Next, using the proposed penetration path planning method, new waypoints were selected simultaneously which satisfy the attitude angle constraints and rank-K fusion criterion of the radar system. Furthermore, for comparative analysis of different algorithms, the conventional A-Star algorithm, bidirectional multilayer A-Star algorithm, and modified A-Star algorithm were utilized to settle the penetration path problem that UAVs experience under various threat scenarios. Finally, the simulation results indicate that the paths obtained by employing the modified algorithm have optimal path costs and higher safety in a 3D complex network radar environment, which show the effectiveness of the proposed path planning scheme.

A Review of Path Planning Method for Mobile Robot

2014 ◽  
Vol 1030-1032 ◽  
pp. 1588-1591 ◽  
Author(s):  
Zong Sheng Wu ◽  
Wei Ping Fu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Grid Method ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Planning Methods ◽  
Basic Ideas ◽  
Artificial Potential Field Method

The ability of a mobile robot to plan its path is the key task in the field of robotics, which is to find a shortest, collision free, optimal path in the various scenes. In this paper, different existing path planning methods are presented, and classified as: geometric construction method, artificial intelligent path planning method, grid method, and artificial potential field method. This paper briefly introduces the basic ideas of the four methods and compares them. Some challenging topics are presented based on the reviewed papers.

scholarly journals A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 99 ◽  
Author(s):  
Haiyan Wang ◽  
Zhiyu Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Mobile Robots ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimal Path ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Elastic Particle

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.

scholarly journals An Improved A-Star Algorithm Considering Water Current, Traffic Separation and Berthing for Vessel Path Planning

2019 ◽  
Vol 9 (6) ◽  
pp. 1057 ◽  
Author(s):  
Chenguang Liu ◽  
Qingzhou Mao ◽  
Xiumin Chu ◽  
Shuo Xie
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Optimal Path ◽  
Water Current ◽  
Path Generation ◽  
Collision Risk ◽  
A Algorithm ◽  
Risk Models ◽  
Planning Methods ◽  
Path Cost

A traditional A-Star (A*) algorithm generates an optimal path by minimizing the path cost. For a vessel, factors of path length, obstacle collision risk, traffic separation rule and manoeuvrability restriction should be all taken into account for path planning. Meanwhile, the water current also plays an important role in voyaging and berthing for vessels. In consideration of these defects of the traditional A-Star algorithm when it is used for vessel path planning, an improved A-Star algorithm has been proposed. To be specific, the risk models of obstacles (bridge pier, moored or anchored ship, port, shore, etc.) considering currents, traffic separation, berthing, manoeuvrability restriction have been built firstly. Then, the normal path generation and the berthing path generation with the proposed improved A-Star algorithm have been represented, respectively. Moreover, the problem of combining the normal path and the berthing path has been also solved. To verify the effectiveness of the proposed A-Star path planning methods, four cases have been studied in simulation and real scenarios. The results of experiments show that the proposed A-Star path planning methods can deal with the problems denoted in this article well, and realize the trade-off between the path length and the navigation safety.

Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation

2017 ◽  
Vol 133 ◽  
pp. 107-115 ◽  
Author(s):  
Ye Li ◽  
Teng Ma ◽  
Pengyun Chen ◽  
Yanqing Jiang ◽  
Rupeng Wang ◽  
...  
Keyword(s):  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Optimal Path ◽  
Underwater Vehicle ◽  
Planning Method ◽  
Optimal Path Planning ◽  
Terrain Matching

Path Planning for Manipulator Robots in Cluttered Environments

2005 ◽  
Author(s):  
Samir Lahouar ◽  
Said Zeghloul ◽  
Lotfi Romdhane
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Planning Method ◽  
Search Process ◽  
Grid Cells ◽  
Heuristic Function ◽  
Search Mode ◽  
Cluttered Environments ◽  
Grid Sampling ◽  
Cluttered Environment

In this paper we propose a new path planning method for robot manipulators in cluttered environments, based on lazy grid sampling. Grid cells are built while searching for the path to the goal configuration. The proposed planner acts in two modes. A depth mode, while the robot is far from obstacles, makes it evolve toward its goal. While a width search mode becomes active when the robot gets close to an obstacle. This method provides the shortest path to go around the obstacle. It also reduces the gap between pre-computed grid methods and lazy grid methods. No heuristic function is needed to guide the search process. An example dealing with a robot in a cluttered environment is presented to show the efficiency of the method.

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