An optimal control-based path planning method for unmanned surface vehicles in complex environments

2022 ◽  
Vol 245 ◽  
pp. 110532
Author(s):  
Dongfang Ma ◽  
Shunfeng Hao ◽  
Weihao Ma ◽  
Huarong Zheng ◽  
Xiuli Xu
Keyword(s):  
Optimal Control ◽  
Path Planning ◽  
Planning Method ◽  
Complex Environments ◽  
Unmanned Surface Vehicles

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 ◽  
...  
Keyword(s):  
Path Planning ◽  
Planning Method ◽  
Complex Environments ◽  
Unmanned Surface Vehicles

Analysis of Aircraft Path Planning Optimal on Carrier Flight Deck

2013 ◽  
Vol 664 ◽  
pp. 1122-1127 ◽  
Author(s):  
Yao Yu Li ◽  
Yi Fan Zhu ◽  
Qun Li
Keyword(s):  
Optimal Control ◽  
Path Planning ◽  
Dynamic Model ◽  
Pseudospectral Method ◽  
Constraint Equation ◽  
Planning Method ◽  
Space Environment ◽  
Polar Coordinates ◽  
Simulation Results ◽  
Flight Deck

Since the limited space and complicated environment, we can't solve this problem by traditional path planning method which taking the aircraft as a particle. Aim at this problem, we introduce the pseudospectral method from the field of optimal control, and indicate the feasibility and advantage based on theory discussion. To simulate the actual path planning on flight deck, we build a carrier deck space environment, establish the collision detecting mechanism based on polar coordinates transforming. Finally an aircraft dynamic model and a simplified obstacle model are designed, thus the path planning constraint equation is given. The veracity and convergence of this method is verified by experiment simulation results.

A fuzzy multi-stage path-planning method for a robot in a dynamic environment with unknown moving obstacles

Robotica ◽  
2014 ◽  
Vol 33 (9) ◽  
pp. 1869-1885 ◽  
Author(s):  
Pooya Mobadersany ◽  
Sohrab Khanmohammadi ◽  
Sehraneh Ghaemi
Keyword(s):  
Path Planning ◽  
Blood Vessels ◽  
Dynamic Environment ◽  
Planning Method ◽  
Planning Problem ◽  
Complex Environments ◽  
Time Intervals ◽  
Moving Obstacles ◽  
Multi Stage ◽  
Static Form

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.

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.

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