Abstract
UAV needs sensor to fly in an environment with obstacles. However, UAV may not be able to move forward when it encounters a large obstacle, or UAV will be in a dangerous state when the sensor fails briefly which disturbed by the environment factors. In order to solve these problems, the following methods are proposed in this paper. Aiming at the first problem, this paper proposes an improved APF method for path planning, and verified by simulation experiments that this method can find the optimal path. Aiming at the second problem, this paper proposes a solution to expand the range of obstacles and dynamically change the distance in the APF repulsion function. It is verified that the UAV can fly safely within the short time of the sensor problem by simulation experiments. In conclusion, this paper has an important reference value for the application of UAV online dynamic path planning in engineering.
Highlights
With the rapid advancement of science and technology, the technology of unmanned aerial vehicle(UAV) has developed rapidly
When encountering a large obstacle, if the UAV tracks a dynamic target point, it may get rid of the obstacle and move on, but this possibility is very small when tracking a fixed target point, so this paper only considers the situation that tracking fixed target point when encountering a large obstacle
This paper mainly studies the path planning of multi-rotor UAV when the sensor is affected by the environment or encountering a large obstacle when tracking fixed or dynamic target point
Summary
With the rapid advancement of science and technology, the technology of unmanned aerial vehicle(UAV) has developed rapidly. The obstacle avoidance sensors used on UAV have higher accuracy, longer detection distances and stronger anti-interference performance. Many scholars proposed a lot advanced path planning algorithms, there are traditional methods such as A* algorithm[1,2,3], APF method[4,5], and Ant Colony algorithm[5,6,7,8], as well as intelligent path planning methods based on Neural Network[9] and Genetic algorithms[10] etc. (1)When the UAV encounters a striped obstacle (such as a wall), the traditional APF method may not work properly. (2)In the case of interference from the external environment, the sensor may have problems such as decreased accuracy and shorter detection range. When the global information is unknown and the sensor is not working properly, the traditional path.
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