Three-dimensional Route Planning of UAV Based on Improved Sparrow Search Algorithm

Zhang, Zhe; Di, Shen; Tang, Xinlei; Zhang, Renmeng

doi:10.1109/icmsp55950.2022.9859027

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…The interpolation is performed by the above three algorithms, and the graphical comparison results by using the original elevation data are shown in Figures A1 and A2 and Table A1 in Appendix B. It can be seen that the mean and variance of the differences between the two-dimensional cubic convolution interpolation and the original elevation are the smallest, and the covariance and correlation coefficient between the two-dimensional cubic convolution interpolation and the original elevation are the largest, after the resolution is reduced, indicating that the two-dimensional cubic convolution interpolation [25] has the highest correlation with the original elevation and the best restoration effect. Therefore, the 2D cubic convolution interpolation algorithm is selected as the interpolation algorithm to adjust the DEM resolution [26].…”

Section: Adjust Map Resolutionmentioning

confidence: 99%

Improved A-Star Path Planning Algorithm in Obstacle Avoidance for the Fixed-Wing Aircraft

Li,

Yu,

Zhang

et al. 2023

Electronics

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The flight management system is a basic component of avionics for modern airliners. However, the airborne flight management system needs to be improved and relies on imports; path planning is the key to the flight management system. Based on the classical A* algorithm, this paper proposes an improved A* path planning algorithm, which solves the problem of low planning efficiency and following a non-smooth path. In order to solve the problem of the large amount of data calculation and long planning time of the classical A* algorithm, a new data structure called a “value table” is designed to replace the open table and close table of the classical A* algorithm to improve the retrieval efficiency, and the Heap sort algorithm is used to optimize the efficiency of node sorting. Aiming at the problem that the flight trajectory is hard to follow, the trajectory smoothing optimization algorithm combined with turning angle limit is proposed. The gray value in the digital map is added to the A* algorithm, and the calculation methods of gray cost, cumulative cost, and estimated cost are improved, which can better meet the constraints of obstacle avoidance. Through the comparative simulation verification of the algorithm, the improved A* algorithm can significantly reduce the path planning time to 1% compared to the classical A* algorithm; it can be seen that the proposed algorithm improves the efficiency of path planning and the smoother planned path, which has obvious advantages compared to the classical A* algorithm.

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Section: Adjust Map Resolutionmentioning

confidence: 99%

Improved A-Star Path Planning Algorithm in Obstacle Avoidance for the Fixed-Wing Aircraft

Li,

Yu,

Zhang

et al. 2023

Electronics

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“…The sparse A* Search algorithm (SAS) is an improved A* algorithm [1]. According to the SAS, the space of the node search can be reduced considering constraints, which can improve the search efficiency of the algorithm.…”

Section: Trajectory Planning Based On Sparse A* Algorithmmentioning

confidence: 99%

“…In addition to the stealth performance of an aircraft itself, such as its use of radarabsorbing materials and a stealthy shape design, devising a trajectory that avoids exposing the high radar cross-section to reduce the radar detection probability is also a crucial method for enhancing aircraft survivability. The process of penetration trajectory planning mainly consists of two parts: the construction of a cost function and the selection of a trajectory-planning algorithm [1].…”

Section: Introductionmentioning

confidence: 99%

“…Traditional trajectory planning algorithms include the A* algorithm, Voronoi graph method, artificial potential field method, fast extended random tree method, optimal control method, dynamic programming method and gradient descent method [1]. And the promising engineering methods include the sparse A* algorithm, particle swarm optimization algorithm and ant colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Stealth Aircraft Penetration Trajectory Planning in 3D Complex Dynamic Environment Based on Sparse A* Algorithm

Guan,

Huang,

Song

et al. 2024

Aerospace

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To find a trajectory with low radar detection probability for stealth aircraft under the assumption of 2D space, performing a rapid turning maneuver is a useful way to reduce the radar detection probability of an aircraft by changing the azimuth angle rapidly to reduce the time of high radar cross-section (RCS) exposure to radar. However, in real flight, not only does the azimuth angle to the radar change rapidly but the elevation angle also changes rapidly, and the change in the radar cross-section is also significant in this process. Based on this premise, this paper established a trajectory planning method based on the sparse A* algorithm in a 3D complex, dynamic environment, called the 3D sparse A* method, based on a log-normal radar model (the 3D-SASLRM method), which considers the RCS statistical uncertainty and the statistical characteristics of the radar signals. Simulations were performed in both simple and complex scenarios. It was concluded that the established 3D-SASLRM method can significantly reduce the radar detection probability. And the essence of reducing under the assumption of 3D space is also to reduce the time of high radar cross-section exposure to radar.

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“…With the continuous progress of science and technology and the development of unmanned systems, unmanned aircraft combat is gradually becoming the mainstream form of modern warfare. Low-altitude penetration [1], as a tactical means, is an important feature of future UAV combat. In an increasingly complex combat environment, unmanned aerial vehicles (UAVs) need to reduce flight altitudes; strategically avoid anti-aircraft fire and radar monitoring with the help of mountain ranges, islands, and other bunkers; and use low-altitude penetration to complete reconnaissance or strike missions, enhance the survival ability of aircrafts, and improve the precision of strikes.…”

Section: Introductionmentioning

confidence: 99%

A Photosensitivity-Enhanced Plant Growth Algorithm for UAV Path Planning

Yang,

Huang,

Gao

et al. 2024

Biomimetics

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With the rise and development of autonomy and intelligence technologies, UAVs will have increasingly significant applications in the future. It is very important to solve the problem of low-altitude penetration of UAVs to protect national territorial security. Based on an S-57 electronic chart file, the land, island, and threat information for an actual combat environment is parsed, extracted, and rasterized to construct a marine combat environment for UAV flight simulation. To address the problem of path planning for low-altitude penetration in complex environments, a photosensitivity-enhanced plant growth algorithm (PEPG) is proposed. Based on the plant growth path planning algorithm (PGPP), the proposed algorithm improves upon the light intensity preprocessing and light intensity calculation methods. Moreover, the kinematic constraints of the UAV, such as the turning angle, are also considered. The planned path that meets the safety flight requirements of the UAV is smoother than that of the original algorithm, and the length is reduced by at least 8.2%. Finally, simulation tests are carried out with three common path planning algorithms, namely, A*, RRT, and GA. The results show that the PEPG algorithm is superior to the other three algorithms in terms of the path length and path quality, and the feasibility and safety of the path are verified via the autonomous tracking flight of a UAV.

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Three-dimensional Route Planning of UAV Based on Improved Sparrow Search Algorithm

Cited by 4 publications

References 10 publications

Improved A-Star Path Planning Algorithm in Obstacle Avoidance for the Fixed-Wing Aircraft

Improved A-Star Path Planning Algorithm in Obstacle Avoidance for the Fixed-Wing Aircraft

Stealth Aircraft Penetration Trajectory Planning in 3D Complex Dynamic Environment Based on Sparse A* Algorithm

A Photosensitivity-Enhanced Plant Growth Algorithm for UAV Path Planning

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