scholarly journals The Online Path Planning Method of UAV Autonomous Inspection in Distribution Network

2021 ◽  
Vol 256 ◽  
pp. 01047
Author(s):  
Li Li ◽  
Hong Zhan ◽  
Yongjing Hao
Keyword(s):  
Path Planning ◽  
Distribution Network ◽  
Dynamic Environment ◽  
Planning Method ◽  
Random Tree ◽  
Mountainous Areas ◽  
Tree Algorithm ◽  
Distribution Lines ◽  
Surrounding Environment ◽  
Autonomous Inspection

In this paper, the problem of online path planning for autonomous inspection of distribution network lines by UAV is studied. Because the distribution lines are mostly distributed around cities, counties and mountainous areas, the lines and their surrounding environment are uncertain and dynamic. These factors will affect the safety of UAV inspection, making the off-line pre-planned path for UAV unavailable. This paper designs an improved iteration random tree algorithm (IRRT) algorithm, which can quickly plan the path of UAV in dynamic environment.

scholarly journals Improved Artificial Potential Field Method Applied for AUV Path Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaojing Fan ◽  
Yinjing Guo ◽  
Hui Liu ◽  
Bowen Wei ◽  
Wenhong Lyu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Potential Field ◽  
Moving Objects ◽  
Dynamic Environment ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Field Function ◽  
Time Path

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.

An Adaptive Rapidly-Exploring Random Tree Algorithm for Assembly Path Planning in Complex Environments

2011 ◽  
Author(s):  
Wei Shang ◽  
Jian-hua Liu
Keyword(s):  
Path Planning ◽  
Performance Improvement ◽  
Random Tree ◽  
Complex Environments ◽  
Tree Algorithm ◽  
The Future ◽  
Constrained Environments ◽  
Significant Performance

We present a refined Rapidly-exploring Random Tree (RRT) algorithm for assembly path planning in complex environments. This algorithm adapts its expansion automatically to explore complex environments with narrow passages and cluttered obstacles more efficiently. In this algorithm, the nodes in the tree are classified by various criterions and different extending values are assigned on them indicating the nearby environment and are used to control the future expansion. A series of tree extending schemes are designed and selectively used based on the attributes of the node and the extending result in each step. We show that the algorithm becomes greedy in constrained environments and promising nodes have higher priority to extend than the non-promising ones. The algorithm is evaluated and applied in assembly path planning. The results show significant performance improvement over the standard RRT planner.

scholarly journals Path planning for coal mine robot to avoid obstacle in gas distribution area

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775150 ◽  
Author(s):  
Xiliang Ma ◽  
Ruiqing Mao
Keyword(s):  
Path Planning ◽  
Coal Mine ◽  
Ant Colony Algorithm ◽  
Dynamic Environment ◽  
Planning Method ◽  
Distribution Area ◽  
Dijkstra Algorithm ◽  
Safety Level ◽  
Planning Methods ◽  
Local Path

As the explosion-proof safety level of coal mine robot has not yet reached the level of intrinsic safety “ia,” therefore, path planning methods for coal mine robot to avoid the dangerous area of gas are necessary. To avoid a secondary explosion when the coal mine robot passes through gas hazard zones, a path planning method is proposed, considering the gas concentration distributions. The path planning method is composed of two steps in total: the global path planning and the local path adjustment. First, the global working path for coal mine robot is planed based on the Dijkstra algorithm and the ant colony algorithm. Second, with consideration of the dynamic environment, when hazardous gas areas distribute over the planed working path again, local path adjustments are carried out with the help of a proposed local path adjustment method. Lastly, experiments are conducted in a roadway after accident, which verify the effectiveness of the proposed path planning method.

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.

scholarly journals Path planning for mobile robot based on modified rapidly exploring random tree method and neural network

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878422 ◽  
Author(s):  
Pengchao Zhang ◽  
Chao Xiong ◽  
Wenke Li ◽  
Xiaoxiong Du ◽  
Chuan Zhao
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Random Tree ◽  
Target Point ◽  
Planning Problem ◽  
Tree Algorithm ◽  
Time Performance ◽  
Path Planning Problem

In the course of the task, the mobile robot should find the shortest and most smooth obstacle-free path to move from the current point to the target point efficiently, which is namely the path planning problem of the mobile robot. After analyzing a large number of planning algorithms, it is found that the combination of traditional planning algorithm and heuristic programming algorithm based on artificial intelligence have outstanding performance. Considering that the basic rapidly exploring random tree algorithm is widely used for some of its advantages, meanwhile there are still defects such as poor real-time performance and rough planned path. So, in order to overcome these shortcomings, this article proposes target bias search strategy and a new metric function taking both distance and angle into account to improve the basic rapidly exploring random tree algorithm, and the neural network is used for curve post-processing to obtain a smooth path. Through simulating in complex environment and comparison with the basic rapidly exploring random tree algorithm, it shows good real-time performance and relatively shorter and smoother planned path, proving that the improved algorithm has good performance in handling path planning problem.

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