Rough level path planning method for a robot using SOFM neural network

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 415-423 ◽  
Author(s):  
Kimmo Pulakka ◽  
Veli Kujanpää
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Industrial Robot ◽  
Planning Method ◽  
Planning Problem ◽  
Test Results ◽  
Feature Map ◽  
Working Environments ◽  
Path Planning Problem

In this paper a path planning method for off-line programming of a joint robot is described. The method can automatically choose the easiest and safest route for an industrial robot from one position to another. The method is based on the use of a Self Organised Feature Map (SOFM) neural network. By using the SOFM neural network the method can adapt to different working environments of the robot. According to test results one can conclude that the SOFM neural network is a useful tool for the path planning problem of a robot.

scholarly journals A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jianjun Ni ◽  
Liuying Wu ◽  
Pengfei Shi ◽  
Simon X. Yang
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Planning Method ◽  
Planning Problem ◽  
Detection Range ◽  
The Real ◽  
Time Path

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

2-Degree-of-freedom Robot Path Planning using Cooperative Neural Fields

1991 ◽  
Vol 3 (3) ◽  
pp. 350-362 ◽  
Author(s):  
Michael Lemmon
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Oscillatory Behavior ◽  
Degree Of Freedom ◽  
Planning Problem ◽  
Neural Fields ◽  
Dynamic Programming Equation ◽  
Two Degree Of Freedom ◽  
Path Planning Problem ◽  
Robot Path

This paper proposes a neural network solution to path planning by two degree-of-freedom (DOF) robots. The proposed network is a two-dimensional sheet of neurons forming a distributed representation of the robot's workspace. Lateral interconnections between neurons are “cooperative,” so that the field exhibits oscillatory behavior. This paper shows how that oscillatory behavior can be used to solve the path-planning problem. The results reported show that the proposed neural network finds the variational solution of Bellman's dynamic programming equation.

Research on Travel Path Planning Based on Group Related Mapping

2014 ◽  
Vol 644-650 ◽  
pp. 5836-5839
Author(s):  
Li Na Tan
Keyword(s):  
Path Planning ◽  
Mapping Method ◽  
Planning Method ◽  
Planning Problem ◽  
Travel Path ◽  
Path Planning Problem

This paper analyzed travel path planning problem. Firstly, it reviewed some references about path planning method and found that those methods were not suit for travel path planning. Secondly, it proposed group related mapping method to solve travel path planning problem. This method had two steps, arranging trips when conflicts were overlooked and rearranging the trips when conflicts were eliminated. Thirdly, to explain the arrangement clearly, it took schedule of ten days travel along the Big Long River as an example. The result showed that the arrangement of all the accessible trips could be worked out during the whole rafting season.

An approach to time-optimal, smooth and collision-free path planning in a two robot arm environment

Robotica ◽  
1996 ◽  
Vol 14 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Bailin Cao ◽  
Gordon I. Dodds ◽  
George W. Irwin
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Industrial Robot ◽  
Planning Problem ◽  
Robot Arm ◽  
Rates Of Change ◽  
Time Optimal ◽  
Time Optimality ◽  
Path Planning Problem ◽  
Effective Collision

SummaryAn approach to time-optimal smooth and collision-free path planning for two industrial robot arms is presented, where path planning and joint trajectory generation are integrated. A suitable objective function, combining the requirements of time optimality and path smoothness, is proposed, which is subject to the continuity of joint trajectories, limits on their rates of change and collision-free constraints. Fast and effective collision detection for the arms is achieved using the Kuhn- Tucker conditions along with the convexity of the distance function and relying on geometrical relationships between cylinders. Nonlinear optimization is used to solve this path planning problem. The feasibility of this method is illustrated both by simulation and by experimental results.

scholarly journals Path planning for mobile robot based on modified rapidly exploring random tree method and neural network

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878422 ◽  
Author(s):  
Pengchao Zhang ◽  
Chao Xiong ◽  
Wenke Li ◽  
Xiaoxiong Du ◽  
Chuan Zhao
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Random Tree ◽  
Target Point ◽  
Planning Problem ◽  
Tree Algorithm ◽  
Time Performance ◽  
Path Planning Problem

In the course of the task, the mobile robot should find the shortest and most smooth obstacle-free path to move from the current point to the target point efficiently, which is namely the path planning problem of the mobile robot. After analyzing a large number of planning algorithms, it is found that the combination of traditional planning algorithm and heuristic programming algorithm based on artificial intelligence have outstanding performance. Considering that the basic rapidly exploring random tree algorithm is widely used for some of its advantages, meanwhile there are still defects such as poor real-time performance and rough planned path. So, in order to overcome these shortcomings, this article proposes target bias search strategy and a new metric function taking both distance and angle into account to improve the basic rapidly exploring random tree algorithm, and the neural network is used for curve post-processing to obtain a smooth path. Through simulating in complex environment and comparison with the basic rapidly exploring random tree algorithm, it shows good real-time performance and relatively shorter and smoother planned path, proving that the improved algorithm has good performance in handling path planning problem.

Optimum path planning of robot arms

Robotica ◽  
1987 ◽  
Vol 5 (4) ◽  
pp. 323-331 ◽  
Author(s):  
V. Braibant ◽  
M. Geradin
Keyword(s):  
Path Planning ◽  
Industrial Robot ◽  
Planning Problem ◽  
Numerical Application ◽  
Optimum Path ◽  
On Line ◽  
Physical Limitations ◽  
Line Path ◽  
Path Planning Problem ◽  
Mixed Nature

SUMMARYThe optimum control of an industrial robot can be achieved by splitting the problem into two tasks: off-line programming of an optimum path, followed by an on-line path tracking.The aim of this paper is to address the numerical solution of the optimum path planning problem. Because of its mixed nature, it can be expressed either in terms of Cartesian coordinates or at joint level.Whatever the approach adopted, the optimum path planning problem can be formulated as the problem of minimizing the overall time (taken as objective function) subject to behavior and side constraints arising from physical limitations and deviation error bounds. The paper proposes a very general optimization algorithm to solve this problem, which is based on the concept of mixed approximation.A numerical application is presented which demonstrates the computational efficiency of the proposed algorithm.

scholarly journals A Re-Entry Path Planning Method for Service Robots Based on Dynamic Inver-Over Evolutionary Algorithm

2019 ◽  
Vol 10 (1) ◽  
pp. 305
Author(s):  
Yong Tao ◽  
Chaoyong Chen ◽  
Tianmiao Wang ◽  
Youdong Chen ◽  
Hegen Xiong ◽  
...  
Keyword(s):  
Path Planning ◽  
Dynamic Properties ◽  
Planning Method ◽  
Service Robots ◽  
Planning Problem ◽  
Time Dynamic ◽  
Coverage Path Planning ◽  
Cleaning Robots ◽  
Changes Over Time ◽  
Path Planning Problem

A re-entry path planning method in omitting areas for service robots is suggested based on dynamic Inver-Over evolutionary algorithms after the robot automatically avoids obstacles. The complete coverage path planning is researched for cleaning service robots. Combined with features of dynamic travelling salesmen problem (DTSP), a local operator is employed for the path planning to enhance real-time dynamic properties of the Inver-Over algorithm. The method addresses the path planning problem that a number of cells undergo dynamic changes over time under work environment of cleaning robots. With simulations and experiments performed, it is discovered that the average relative error is 2.2% between the re-entry path planning and the best path, which validates the effectiveness and feasibility of the method.

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