scholarly journals Fast Path Planning for On-Water Automatic Rescue Intelligent Robot Based on Constant Thrust Artificial Fluid Method

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yongqiang Qi ◽  
Yi Ke
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Interpolation Method ◽  
Three Dimensional ◽  
Intelligent Robot ◽  
Perturbation Matrix ◽  
Fluid Field ◽  
Environment Model ◽  
Simulation Results ◽  
Novel Algorithm

In this paper, fast path planning of on-water automatic rescue intelligent robot is studied based on the constant thrust artificial fluid method. First, a three-dimensional environment model is established, and then the kinematics equation of the robot is given. The constant thrust artificial fluid method based on the isochronous interpolation method is proposed, and a novel algorithm of constant thrust fitting is researched through the impulse compensation. The effect of obstacles on original fluid field is quantified by the perturbation matrix, and the streamlines can be regarded as the planned path. Simulation results demonstrate the effectiveness of this method by comparing with A-star algorithm and ant colony algorithm. It is proved that the planned path can avoid all obstacles smoothly and swiftly and reach the destination eventually.

scholarly journals Three-Dimensional Path Planning of Constant Thrust Unmanned Aerial Vehicle Based on Artificial Fluid Method

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yongqiang Qi ◽  
Shuai Li ◽  
Yi Ke
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Interpolation Method ◽  
Three Dimensional ◽  
Planning Problem ◽  
Perturbation Matrix ◽  
Fluid Field ◽  
Control Scheme ◽  
Aerial Vehicle ◽  
Tangential Vector

In this paper, a three-dimensional path planning problem of an unmanned aerial vehicle under constant thrust is studied based on the artificial fluid method. The effect of obstacles on the original fluid field is quantified by the perturbation matrix, the streamlines can be regarded as the planned path for the unmanned aerial vehicle, and the tangential vector and the disturbance matrix of the artificial fluid method are improved. In particular, this paper addresses a novel algorithm of constant thrust fitting which is proposed through the impulse compensation, and then the constant thrust switching control scheme based on the isochronous interpolation method is given. It is proved that the planned path can avoid all obstacles smoothly and swiftly and reach the destination eventually. Simulation results demonstrate the effectiveness of this method.

scholarly journals UAV Path Planning of Combining Ant Colony and Beetle Antennae Algorithm Using Intelligent Wireless Communication

2021 ◽  
Vol 2083 (2) ◽  
pp. 022058
Author(s):  
Le Xu ◽  
Wenlong Zhao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Three Dimensional ◽  
Ant Colony ◽  
Fusion Algorithm ◽  
Third Order ◽  
Environment Model ◽  
Speed Up

Abstract In order to settle the problem of UAV path planning under mountain, an algorithm which based on the combination of ant colony algorithm and beetle antennae algorithm is proposed. Three dimensional environment model is established and objective function is constructed. It used ant colony algorithm to initialize the search path and the particle coordinates of all the next steps are updated by the beetle antennae algorithm. The improved algorithm adopted a new step update rule to speed up the convergence of the algorithm and used third-order B-spline interpolation method to smooth the path. Simulation results show that improved fusion algorithm has faster convergence speed and high stability by comparing with other algorithms under the same conditions, which verifies its effectiveness.

Path Planning and Replanning for Intelligent Robot Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 390 ◽  
pp. 495-499 ◽  
Author(s):  
Bi Wei Tang ◽  
Zhan Xia Zhu ◽  
Qun Fang ◽  
Wei Hua Ma
Keyword(s):  
Path Planning ◽  
Objective Function ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Intelligent Robot ◽  
Intelligent Robots ◽  
Simulation Results ◽  
Improved Ant Colony Algorithm ◽  
Self Adaptive

The effectiveness of path planning and path replanning for intelligent robot using improved ant colony algorithm is explored in this paper. For the purpose of avoiding falling into local optimum and preventing iterative stagnant, this paper describes a new algorithm named stochastic self-adaptive ant colony algorithm to improve the basic ant colony algorithm. Based on the improved ant colony algorithm, the approaches of path planning and path replanning are presented in this paper. Aiming at improving the speed of the algorithm and simplifying the objective function of traditional path planning, this paper presents a principle of eliminating the path nodes .Finally, some constrast emulators are designed.The simulation results proves that the improved ant colony algorithm has strong adaptability in intelligent robots path path planning and replanning.

Intelligent Three-dimensional Node Localization Algorithm Using Dynamic Path Planning

2021 ◽  
Vol 14 ◽  
Author(s):  
Songhao Jia ◽  
Cai Yang ◽  
Xing Chen ◽  
Yan Liu ◽  
Fangfang Li
Keyword(s):  
Path Planning ◽  
Dynamic Stiffness ◽  
Three Dimensional ◽  
Localization Algorithm ◽  
Node Localization ◽  
Positioning Accuracy ◽  
Dynamic Path Planning ◽  
Anchor Nodes ◽  
Simulation Results ◽  
Dynamic Path

Background: In the applications of wireless sensor network technology, three-dimensional node location technology is crucial. The process of node localization has some disadvantages, such as the uneven distribution of anchor nodes and the high cost of the network. Therefore, the mobile anchor nodes are introduced to effectively solve accurate positioning. Objective: Considering the estimated distance error, the received signal strength indication technology is used to optimize the measurement of the distance. At the same time, dynamic stiffness planning is introduced to increase virtual anchor nodes. Moreover, the bird swarm algorithm is also used to solve the optimal location problem of nodes. Method: Firstly, the dynamic path is introduced to increase the number of virtual anchor nodes. At the same time, the improved RSSI distance measurement technology is introduced to the node localization. Then, an intelligent three-dimensional node localization algorithm based on dynamic path planning is proposed. Finally, the proposed algorithm is compared with similar algorithms through simulation experiments. Results: Simulation results show that the node coordinates obtained by the proposed algorithm are more accurate, and the node positioning accuracy is improved. The execution time and network coverage of the algorithm are better than similar algorithms. Conclusion: The proposed algorithm significantly improves the accuracy of node positioning. However, the traffic of the algorithm is increased. A little increase in traffic in exchange for positioning accuracy is worthy of recognition. The simulation results show that the proposed algorithm is robust and can be implemented and promoted in the future.

Path planning for intelligent robot based on switching local evolutionary PSO algorithm

2016 ◽  
Vol 36 (2) ◽  
pp. 120-126 ◽  
Author(s):  
Nianyin Zeng ◽  
Hong Zhang ◽  
Yanping Chen ◽  
Binqiang Chen ◽  
Yurong Liu
Keyword(s):  
Markov Chain ◽  
Path Planning ◽  
Three Dimensional ◽  
Grid Method ◽  
Pso Algorithm ◽  
Planning Problem ◽  
Homogeneous Markov Chain ◽  
Intelligent Robot ◽  
Environment Modeling ◽  
Content Type

Purpose This paper aims to present a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for path planning of intelligent robot when having obstacles in the environment. Design/methodology/approach The three-dimensional path surface of the intelligent robot is decomposed into a two-dimensional plane and the height information in z axis. Then, the grid method is exploited for the environment modeling problem. After that, a recently proposed switching local evolutionary PSO (SLEPSO) based on non-homogeneous Markov chain and DE is analyzed for the path planning problem. The velocity updating equation of the presented SLEPSO algorithm jumps from one mode to another based on the non-homogeneous Markov chain, which can overcome the contradiction between local and global search. In addition, DE mutation and crossover operations can enhance the capability of finding a better global best particle in the PSO method. Findings Finally, the SLEPSO algorithm is successfully applied to the path planning in two different environments. Comparing with some well-known PSO algorithms, the experiment results show the feasibility and effectiveness of the presented method. Originality/value Therefore, this can provide a new method for the area of path planning of intelligent robot.

Path Planning of Robot Based on Ant Colony Optimization Algorithm

2014 ◽  
Vol 614 ◽  
pp. 199-202 ◽  
Author(s):  
Bao Ming Shan ◽  
De Xiang Zhang
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Robot Path Planning ◽  
Ant Colony Optimization Algorithm ◽  
Simulation Results ◽  
Robot Path

This paper presents a method for robot path planning based on ant colony optimization algorithm, in order to resolve the weakness of ant colony algorithm such as slow convergence rate and easy to fall into local optimum and traps. This method uses anti-potential field to make the robot escape from them smoothly, and at the end of each cycle, uses the way of judge first and then hybridization to optimize the algorithm. Finally, the simulation results show that the performance of the algorithm has been improved, and proved that the optimization algorithm is valid and feasible.

Path Planning for the Chassis of Duct-Cleaning Robot Based on Ant Colony Algorithm

2012 ◽  
Vol 190-191 ◽  
pp. 715-718
Author(s):  
Qian Zhu ◽  
Wei Sun ◽  
Zhi Wei Zhou ◽  
Su Wei Zhang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Simulation Experiments ◽  
Environment Model ◽  
Cleaning Robot ◽  
Duct Cleaning Robot ◽  
Duct Cleaning

For the path planning for the chassis of duct cleaning robot in an obstacle environment, ant colony algorithm and grid method are adopted to achieve an optimal path between two different arbitrary points and establish the environment model. The results of computer simulation experiments demonstrate the effectiveness of ant colony algorithm applied in path planning for the chassis of duct cleaning robot.

A Path-Planning Algorithm for AGV Based on the Combination between Ant Colony Algorithm and Immune Regulation

2011 ◽  
Vol 422 ◽  
pp. 3-9 ◽  
Author(s):  
Jian Zhong Huang ◽  
Yu Wan Cen
Keyword(s):  
Path Planning ◽  
Immune Regulation ◽  
Ant Colony Algorithm ◽  
Visibility Graph ◽  
Ant Colony ◽  
Environment Modeling ◽  
Environment Model ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

For the demand of AGV’s environment modeling and path-planning,the paper discusses how to establish static environment model of visibility graph and proposes a visibility table method.Moreover,based on the environment modeling,we put forward a new kind of global path-planning algorithm by the combination between ant colony algorithm and immune regulation.

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