Constrained Motion Planning in Discrete State Spaces

2006 ◽  
pp. 269-280 ◽  
Author(s):  
Mihail Pivtoraiko ◽  
Alonzo Kelly
Keyword(s):  
Motion Planning ◽  
Constrained Motion ◽  
State Spaces ◽  
Discrete State

scholarly journals Exact and fully symbolic verification of linear hybrid automata with large discrete state spaces

2012 ◽  
Vol 77 (10-11) ◽  
pp. 1122-1150 ◽  
Author(s):  
Werner Damm ◽  
Henning Dierks ◽  
Stefan Disch ◽  
Willem Hagemann ◽  
Florian Pigorsch ◽  
...  
Keyword(s):  
Hybrid Automata ◽  
State Spaces ◽  
Discrete State ◽  
Linear Hybrid Automata ◽  
Symbolic Verification

scholarly journals The Repetition Roadmap for Repetitive Constrained Motion Planning

2018 ◽  
Vol 3 (4) ◽  
pp. 3884-3891 ◽  
Author(s):  
Peter Lehner ◽  
Alin Albu-Schaffer
Keyword(s):  
Motion Planning ◽  
Constrained Motion

scholarly journals A sampling-based optimized algorithm for task-constrained motion planning

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984737 ◽  
Author(s):  
Kai Mi ◽  
Haojian Zhang ◽  
Jun Zheng ◽  
Jianhua Hu ◽  
Dengxiang Zhuang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Sampling Methods ◽  
Tracking Error ◽  
Joint Space ◽  
Planning Problem ◽  
Redundant Manipulators ◽  
Task Space ◽  
Constrained Motion ◽  
Smooth Path ◽  
Planning Algorithm

We consider a motion planning problem with task space constraints in a complex environment for redundant manipulators. For this problem, we propose a motion planning algorithm that combines kinematics control with rapidly exploring random sampling methods. Meanwhile, we introduce an optimization structure similar to dynamic programming into the algorithm. The proposed algorithm can generate an asymptotically optimized smooth path in joint space, which continuously satisfies task space constraints and avoids obstacles. We have confirmed that the proposed algorithm is probabilistically complete and asymptotically optimized. Finally, we conduct multiple experiments with path length and tracking error as optimization targets and the planning results reflect the optimization effect of the algorithm.

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