UAV path planning is one of the key issues in the field of UAV applications. This paper proposes a UAV path planning method based on IRM (Informed RRTstar Method) and RRTstar (Rapidly-exploring Random Tree star) algorithms, and uses Matlab to implement it. This method achieves efficient and reliable UAV path planning by combining the heuristic search of IRM and the fast exploration capability of RRTstar. The experimental results show that this method can effectively plan the path of the UAV in a complex environment, and has a high path planning success rate and path quality.
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1 Introduction
The wide application of UAV makes UAV path planning become one of the research hotspots. The goal of UAV path planning is to find a safe and efficient path so that the UAV can reach the target point from the starting point. However, due to the complexity of the environment and the dynamics of UAVs, the path planning problem becomes very challenging. Therefore, it is of great significance to propose an efficient and reliable UAV path planning method.
2. Related work
In the past decades, researchers have proposed many UAV path planning algorithms. Among them, the RRT (Rapidly-exploring Random Tree) algorithm is a commonly used and effective path planning algorithm. However, the RRT algorithm has certain problems when dealing with complex environments and high-dimensional state spaces. In order to solve these problems, researchers have proposed many improved RRT algorithms, such as RRTstar algorithm and IRM algorithm.
3. Method introduction
This paper proposes a UAV path planning method based on IRM and RRTstar algorithms. First, use the IRM algorithm to model and represent the environment, and divide the environment into discrete grids. Then, use the RRTstar algorithm to search for paths on discrete grids. The RRTstar algorithm realizes the rapid exploration of the environment by continuously expanding the tree structure. Finally, according to the path quality evaluation function, the path with high path planning success rate and good path quality is selected.
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RRT* starts with an empty tree and adds a single node corresponding to the initial state.
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It finds a solution to the motion planning problem.
4. Experimental results
On the Matlab platform, we used a set of real UAV scene data to conduct experiments. Experimental results show that the proposed method can efficiently plan UAV paths in complex environments, and has a high path planning success rate and path quality.
Threshold = 3, Step size : 5, Nodes: 1000 => Cost: 205, Time: 6 sec：
Nodes: 2000 => Cost: 210, Time: 18 sec：
Nodes: 5000 => Cost 208, Time: 104 sec：
Nodes: 8000 => Cost 206, Time: 256 sec：
5. Source code + data + PPT + report download
UAV path planning based on Matlab using IRM and RRTstar (source code + data + description + report + PPT).rar: https://download.csdn.net/download/m0_62143653/88282567