Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment

2015 ◽  
Vol 47 ◽  
pp. 269-279 ◽  
Author(s):  
Peng Yao ◽  
Honglun Wang ◽  
Zikang Su
Keyword(s):  
Path Planning ◽  
Target Tracking ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Unmanned Aerial Vehicle ◽  
Dynamic Environment ◽  
Complex Dynamic ◽  
Time Path ◽  
Aerial Vehicle

scholarly journals Real-Time Path Planning for Multi-DoF Manipulators in Dynamic Environment

10.5772/5749 ◽  
2006 ◽  
Vol 3 (2) ◽  
pp. 20 ◽  
Author(s):  
Samir Lahouar ◽  
Said Zeghloul ◽  
Lotfi Romdhane
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Time Path

scholarly journals Information geometry-based action decision-making for target tracking by fixed-wing unmanned aerial vehicle

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878706 ◽  
Author(s):  
Yunyun Zhao ◽  
Xiangke Wang ◽  
Yirui Cong ◽  
Lincheng Shen
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Markov Decision Process ◽  
Decision Process ◽  
Information Geometry ◽  
Markov Decision ◽  
Aerial Vehicle ◽  
Partially Observable Markov ◽  
Partially Observable

In this article, we study the ground moving target tracking problem for a fixed-wing unmanned aerial vehicle equipped with a radar. This problem is formulated in a partially observable Markov process framework, which contains the following two parts: in the first part, the unmanned aerial vehicle utilizes the measurements from its radar and employs a Kalman filter to estimate the target’s real-time location; in the second part, the unmanned aerial vehicle optimizes its trajectory in a real-time manner so that the radar’s measurements can include more useful information. To solve the trajectory optimization problem, we proposed an information geometry-based partially observable Markov decision process method. Specifically, the cumulative amount of information in the observation is represented by Fisher information of information geometry, and acts as the criterion of the partially observable Markov decision process problem. Furthermore, to guarantee the real-time performance, an important trade-off between the optimality and computation cost is made by an approximate receding horizon approach. Finally, simulation results corroborate the accuracy and time-efficiency of our proposed method and also show our advantage in computation time compared to existing methods.

Real-time path planning and obstacle avoidance for RAIS: an autonomous underwater vehicle

2001 ◽  
Vol 26 (2) ◽  
pp. 216-227 ◽  
Author(s):  
G. Antonelli ◽  
S. Chiaverini ◽  
R. Finotello ◽  
R. Schiavon
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Time Path

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.

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