A fast collision-free motion planning method for underactuated robots based on genetic algorithm

Author(s):  
Qingbo Liu ◽  
Yueqing Yu ◽  
Liying Su ◽  
Qixiao Xia
2021 ◽  
Vol 185 ◽  
pp. 106151
Author(s):  
Lei Ye ◽  
Jieli Duan ◽  
Zhou Yang ◽  
Xiangjun Zou ◽  
Mingyou Chen ◽  
...  
Keyword(s):  

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Peng Cai ◽  
Xiaokui Yue ◽  
Hongwen Zhang

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.


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