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

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.

Download Full-text

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

Mekhatronika Avtomatizatsiya Upravlenie ◽

10.17587/mau.21.470-479 ◽

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.

Download Full-text

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

Volume 2: 34th Annual Mechanisms and Robotics Conference, Parts A and B ◽

10.1115/detc2010-28987 ◽

2010 ◽

Cited By ~ 1

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.

Download Full-text