Free Space Mapping and Motion Planning in Configuration Space for Mobile Manipulators

2007 ◽  
Author(s):  
James Ward ◽  
Jayantha Katupitiya
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Free Space ◽  
Space Mapping ◽  
Mobile Manipulators

Motion Planning in Plant CAD Systems

1991 ◽  
Author(s):  
Koichi Kondo ◽  
Koichi Ohtomi
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Free Space ◽  
Nuclear Power ◽  
Outer Boundary ◽  
Global Strategy ◽  
Planning Problem ◽  
Cad System ◽  
Local Strategy ◽  
Stationary Obstacles

Abstract This paper discusses a general and efficient method for solving the motion planning problem defined as checking the existence of a collision-free path among known stationary obstacles, and also presents its application to a plant CAD system. The basic approach taken in this method is to restrict the free space referred to in path searching and to avoid executing unnecessary collision detections. The configuration space is equally quantized into cells by placing a regular grid, and two new search strategies which enumerate restricted cells are introduced for realizing this method. One is a local strategy which enumerates free space cells only along the boundary of the free space in the configuration space. Another is a global strategy which finds the outer boundary of the free space. This method has been actually implemented and has been applied to an example in a nuclear power plant.

ADD-RRV for motion planning in complex environments

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Peng Cai ◽  
Xiaokui Yue ◽  
Hongwen Zhang
Keyword(s):  
Principal Component Analysis ◽  
Motion Planning ◽  
Configuration Space ◽  
Principal Component ◽  
Component Analysis ◽  
Planning Method ◽  
Complex Environments ◽  
Local Configuration ◽  
Narrow Passage

Abstract In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.

Solving the motion planning problem by using neural networks

Robotica ◽  
1994 ◽  
Vol 12 (4) ◽  
pp. 323-333 ◽  
Author(s):  
R.H.T. Chan ◽  
P.K.S. Tam ◽  
D.N.K. Leung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Planning ◽  
Configuration Space ◽  
Logic Gates ◽  
Moving Object ◽  
Planning Problem ◽  
Processing Unit ◽  
Configuration Point ◽  
Motion Planning Problem

SUMMARYThis paper presents a new neural networks-based method to solve the motion planning problem, i.e. to construct a collision-free path for a moving object among fixed obstacles. Our ‘navigator’ basically consists of two neural networks: The first one is a modified feed-forward neural network, which is used to determine the configuration space; the moving object is modelled as a configuration point in the configuration space. The second neural network is a modified bidirectional associative memory, which is used to find a path for the configuration point through the configuration space while avoiding the configuration obstacles. The basic processing unit of the neural networks may be constructed using logic gates, including AND gates, OR gates, NOT gate and flip flops. Examples of efficient solutions to difficult motion planning problems using our proposed techniques are presented.

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