Robot motion planning by a hierarchical search on a modified discretized configuration space

2002 ◽  
Author(s):  
A. Autere ◽  
J. Lehtinen
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Hierarchical Search

ROBOT MOTION PLANNING BY APPROXIMATION OF OBSTACLES IN CONFIGURATION SPACE

2005 ◽  
Vol 38 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Martin Ruehl ◽  
Hubert Roth
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Robot Motion ◽  
Robot Motion Planning

Quadtree decomposition of configuration space for robot motion planning

1999 ◽  
Author(s):  
J. Kumpel ◽  
J. Sparbert ◽  
B. Tibken ◽  
E. P. Hofer
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Quadtree Decomposition

scholarly journals CATEGORY AND TOPOLOGICAL COMPLEXITY OF THE CONFIGURATION SPACE

2019 ◽  
Vol 100 (3) ◽  
pp. 507-517
Author(s):  
CESAR A. IPANAQUE ZAPATA
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Rigid Bodies ◽  
Robot Motion ◽  
Planning Problem ◽  
Topological Complexity ◽  
Robot Motion Planning ◽  
Spatial Motion ◽  
Lusternik Schnirelmann ◽  
Homotopy Invariants

The Lusternik–Schnirelmann category cat and topological complexity TC are related homotopy invariants. The topological complexity TC has applications to the robot motion planning problem. We calculate the Lusternik–Schnirelmann category and topological complexity of the ordered configuration space of two distinct points in the product $G\times \mathbb{R}^{n}$ and apply the results to the planar and spatial motion of two rigid bodies in $\mathbb{R}^{2}$ and $\mathbb{R}^{3}$ respectively.

Offsetting obstacles of any shape for robot motion planning

Robotica ◽  
2014 ◽  
Vol 33 (4) ◽  
pp. 865-883
Author(s):  
Md Nasir Uddin Laskar ◽  
Hoang Huu Viet ◽  
Seung Yoon Choi ◽  
Ishtiaq Ahmed ◽  
Sungyoung Lee ◽  
...  
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Linear Time ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Efficient Approach ◽  
Intersection Detection ◽  
Circular Arcs ◽  
Offset Curve ◽  
Number Of Segments

SUMMARYWe present an algorithm for offsetting the workspace obstacles of a circular robot. Our method has two major steps: It finds the raw offset curve for both lines and circular arcs, and then removes the global invalid loops to find the final offset. To generate the raw offset curve and remove global invalid loops, O(n) and O((n+k)log m) computational times are needed respectively, where n is the number of vertices in the original polygon, k is the number of self-intersections and m is the number of segments in the raw offset curve, where m ≤ n. Any local invalid loops are removed before generating the raw offset curve by invoking a pair-wise intersection detection test (PIDT). In the PIDT, two intersecting entities are checked immediately after they are computed, and if the test is positive, portions of the intersecting segments are removed. Our method works for conventional polygons as well as the polygons that contain circular arcs. Our algorithm is simple and very fast, as each sub-process of the algorithm can be completed in linear time except the last one, which is nearly linear. Therefore, the overall complexity of the algorithm is nearly linear. By applying our simple and efficient approach, offsetting obstacles of any shape make it possible to construct a configuration space that ensures optimized motion planning.

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