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.

Application of Particle Swarm Optimization to Robot Path Planning Problem

2010 ◽  
Author(s):  
Masakazu Kobayashi ◽  
Higashi Masatake
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Particle Swarm ◽  
Degree Of Freedom ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Swarm Optimization ◽  
Articulated Robot ◽  
Path Planning Problem ◽  
Robot Path

A robot path planning problem is to produce a path that connects a start configuration and a goal configuration while avoiding collision with obstacles. To obtain a path for robots with high degree of freedom of motion such as an articulated robot efficiently, sampling-based algorithms such as probabilistic roadmap (PRM) and rapidly-exploring random tree (RRT) were proposed. In this paper, a new robot path planning method based on Particle Swarm Optimization (PSO), which is one of heuristic optimization methods, is proposed in order to improve efficiency of path planning for a wider range of problems. In the proposed method, a group of particles fly through a configuration space while avoiding collision with obstacles and a collection of their trajectories is regarded as a roadmap. A velocity of each particle is updated for every time step based on the update equation of PSO. After explaining the details of the proposed method, this paper shows the comparisons of efficiency between the proposed method and RRT for 2D maze problems and then shows application of the proposed method to path planning for a 6 degree of freedom articulated robot.

Three Dimensional Robot Path Planning With Workspace Considerations

1992 ◽  
Author(s):  
C. Y. Liu ◽  
R. W. Mayne
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Optimization Methods ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Recursive Quadratic Programming ◽  
Six Degree Of Freedom ◽  
Nonlinear Programming Method ◽  
Path Planning Problem ◽  
Robot Path

Abstract This paper considers the problem of robot path planning by optimization methods. It focuses on the use of recursive quadratic programming (RQP) for the optimization process and presents a formulation of the three dimensional path planning problem developed for compatibility with the RQP selling. An approach 10 distance-to-contact and interference calculations appropriate for RQP is described as well as a strategy for gradient computations which are critical to applying any efficient nonlinear programming method. Symbolic computation has been used for general six degree-of-freedom transformations of the robot links and to provide analytical derivative expressions. Example problems in path planning are presented for a simple 3-D robot. One example includes adjustments in geometry and introduces the concept of integrating 3-D path planning with geometric design.

scholarly journals Research and Implementation of Robot Path Planning Based onVSLAM

2018 ◽  
Vol 160 ◽  
pp. 06004
Author(s):  
Zi-Qiang Wang ◽  
He-Gen Xu ◽  
You-Wen Wan
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Planning Problem ◽  
A Algorithm ◽  
Algorithm Efficiency ◽  
Grid Map ◽  
Localization And Mapping ◽  
Improve Algorithm ◽  
Path Planning Problem ◽  
Robot Path

In order to solve the problem of warehouse logistics robots planpath in different scenes, this paper proposes a method based on visual simultaneous localization and mapping (VSLAM) to build grid map of different scenes and use A* algorithm to plan path on the grid map. Firstly, we use VSLAMto reconstruct the environment in three-dimensionally. Secondly, based on the three-dimensional environment data, we calculate the accessibility of each grid to prepare occupied grid map (OGM) for terrain description. Rely on the terrain information, we use the A* algorithm to solve path planning problem. We also optimize the A* algorithm and improve algorithm efficiency. Lastly, we verify the effectiveness and reliability of the proposed method by simulation and experimental results.

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.

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