Path Planning Method Based on D* lite Algorithm for Unmanned Surface Vehicles in Complex Environments

2021 ◽  
Vol 35 (3) ◽  
pp. 372-383
Author(s):  
Yan-long Yao ◽  
Xiao-feng Liang ◽  
Ming-zhi Li ◽  
Kai Yu ◽  
Zhe Chen ◽  
...  
2022 ◽  
Vol 245 ◽  
pp. 110532
Author(s):  
Dongfang Ma ◽  
Shunfeng Hao ◽  
Weihao Ma ◽  
Huarong Zheng ◽  
Xiuli Xu

Robotica ◽  
2014 ◽  
Vol 33 (9) ◽  
pp. 1869-1885 ◽  
Author(s):  
Pooya Mobadersany ◽  
Sohrab Khanmohammadi ◽  
Sehraneh Ghaemi

SUMMARYPath planning is one of the most important fields in robotics. Only a limited number of articles have proposed a practical way to solve the path-planning problem with moving obstacles. In this paper, a fuzzy path-planning method with two strategies is proposed to navigate a robot among unknown moving obstacles in complex environments. The static form of the environment is assumed to be known, but there is no prior knowledge about the dynamic obstacles. In this situation, an online and real-time approach is essential for avoiding collision. Also, the approach should be efficient in natural complex environments such as blood vessels. To examine the efficiency of the proposed algorithm, a drug delivery nanorobot moving in a complex environment (blood vessels) is supposed. The Monte Carlo simulation with random numbers is used to demonstrate the efficiency of the proposed approach, where the dynamic obstacles are assumed to appear in exponentially distributed random time intervals.


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.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 135513-135523
Author(s):  
Qingfeng Yao ◽  
Zeyu Zheng ◽  
Liang Qi ◽  
Haitao Yuan ◽  
Xiwang Guo ◽  
...  

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