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.

scholarly journals Two-degree-of-freedom manipulator path planning based on zeroing neural network

2020 ◽  
Vol 309 ◽  
pp. 04005
Author(s):  
Yan Li ◽  
Keping Liu
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Shortest Path ◽  
Lyapunov Method ◽  
Optimal Path ◽  
Shortest Path Problem ◽  
Optimal Planning ◽  
Optimal Path Planning ◽  
The Stability ◽  
Two Degree Of Freedom

In this paper, the shortest path problem of manipulator path planning is transformed into a linear programming problem, and solved by zeroing neural network (ZNN). Firstly, the method of constructing the zeroing neural dynamics is given, and the ZNN model is constructed for shortest path problem of manipulator. Then, the Lyapunov method is utilized to prove the stability of the ZNN model. Finally, the ZNN model is applied to the path planning of manipulator to generate an optimal planning path. The simulation results show that the proposed method can effectively realize the optimal path planning of the manipulator.

Optimal Path Planning Method of Marine Sailboat Based on Fuzzy Neural Network

2019 ◽  
Vol 93 (sp1) ◽  
pp. 911 ◽  
Author(s):  
Keshuang Sun ◽  
Jiezhong Wu ◽  
Zhenyu Sun ◽  
Zhongwang Cao
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Fuzzy Neural Network ◽  
Optimal Path ◽  
Planning Method ◽  
Optimal Path Planning ◽  
Fuzzy Neural

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.

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

Finding the Shortest Path for a Mobile Robot With Two Actuated Spoke Wheels Based on Variable Kinematic Configurations

2010 ◽  
Author(s):  
Ya Wang ◽  
Dennis Hong
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Shortest Path ◽  
Optimal Path ◽  
Curvature Radius ◽  
Planning Strategy ◽  
Optimal Path Planning ◽  
Path Geometry ◽  
Motion Characteristics ◽  
Motion Strategy

Strategies for finding the shortest path for a mobile robot with two actuated spoke wheels based on variable kinematic configurations are presented in this paper. The optimal path planning strategy proposed here integrate the traditional constrained path planning tools and the unique kinematic configuration spaces of the mobile robot IMPASS (Intelligent Mobility Platform with Actuated Spoke System). IMPASS utilizes a unique mobility concept of stretching in or out individually actuated spokes in order to perform variable curvature radius steering using changing kinematic configuration during its movement. Due to this unique motion strategy, various kinematic topologies produce specific motion characteristics in the way of curvature radius-variable steering. Instead of traditional differential drive or Ackerman steering locomotion, combinational path geometry methods, Dubins’ curve and Reeds and Shepp’s curve are applied to classify optimal paths into known permutations of sequences consisting of various kinematic configurations. Numerical simulation is given to verify the analytical solutions provided by using Lagrange Multiplier.

scholarly journals Research on the Food Logistic Optimal Path Planning Method of Improving the Genetic Algorithm

2016 ◽  
Vol 11 (4) ◽  
pp. 269-273
Author(s):  
Li Si ◽  
Wang Yuan ◽  
Li Xinzhong ◽  
Liu Shenyang ◽  
Li Zhen
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Optimal Path ◽  
Planning Method ◽  
Optimal Path Planning

Path planning method for unmanned underwater vehicles eliminating effect of currents based on artificial potential field

2021 ◽  
pp. 1-13
Author(s):  
Danjie Zhu ◽  
Simon X. Yang
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Optimal Path ◽  
Underwater Vehicles ◽  
Planning Method ◽  
Ocean Currents ◽  
Artificial Potential Field ◽  
Intelligent Algorithm ◽  
Unmanned Underwater Vehicles ◽  
Optimal Path Planning

Abstract To eliminate the effect of ocean currents for optimal path planning for unmanned underwater vehicles (UUVs) in the underwater environment, an intelligent algorithm is designed and proposed in this paper. The algorithm consists of two parts: an artificial potential field-based algorithm that derives the shortest path and avoids collision accidents; and an adjusting function that eliminates the effect of ocean currents. The planning results of the intelligent algorithm are presented in detail, and compared with the conventional algorithm that does not consider the effect of currents. The effectiveness of the optimised path planning method given in this paper is proved.

scholarly journals Simulation design of trajectory planning robot manipulator

2019 ◽  
Vol 8 (1) ◽  
pp. 196-205 ◽  
Author(s):  
Wahyu S. Pambudi ◽  
Enggar Alfianto ◽  
Andy Rachman ◽  
Dian Puspita Hapsari
Keyword(s):  
Neural Network ◽  
Control System ◽  
Robot Manipulator ◽  
Optimal Path ◽  
Average Error ◽  
Target Point ◽  
Simulation Design ◽  
Self Organizing Maps ◽  
Network Process ◽  
Fuzzy Pd

Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.

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