UAV autonomous path optimization simulation based on radar tracking prediction

Wang, Bo; Jin, Bao; Zhang, Li; Sheng, Qinghong

doi:10.1186/s13638-018-1260-9

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…A study for generating 3D flight paths for a swarm of cooperating UAVs flying in a formation having a prespecified shape, in the presence of polygonal obstacles, no-fly zones, and other non-cooperative aircraft No [109] 2019 A study on Dijkstra's algorithm and a heuristic algorithm for the path planning of a UAV and comparing the results under different configurations Yes [110] 2019 A framework of autonomous bridge inspection using a UAV which consists of a six-step process and uses several sensors, cameras, and LiDAR No [111] 2018 A method to find the optimal path for a UAV flight considering 3 cost functions: path security cost, length cost, and smoothness cost Yes [112] 2018 A survey on computational-intelligence-based UAV path planning considering offline and online planning, and 2D and 3D models No [113] 2018…”

Section: Yes [75] 2021mentioning

confidence: 99%

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2024

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Section: Yes [75] 2021mentioning

confidence: 99%

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2024

CTCSE

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“…Wang et al used Kalman filtering to predict the position of a target and the ACO algorithm to optimize the tracking path of multiple targets, improving the tracking ability of a UAV to multiple dynamic targets. However, the algorithm did not study scenes with obstacles 37 . A dynamic path planning algorithm based on obstacles’ position prediction and modified APF—HOAP proposed by Feng et al, could effectively deal with the influence of dynamic obstacles.…”

Section: Related Workmentioning

confidence: 99%

Piecewise-potential-field-based path planning method for fixed-wing UAV formation

Fang

Yao

Zhu

et al. 2023

Sci Rep

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The multi-UAV path planning method based on artificial potential field (APF) has the advantage of rapid processing speed and the ability to deal with dynamic obstacles, though some problems remain—such as a lack of consideration of the initial heading constraint of the UAVs, making it easy to fall into a local minimum trap, and the path not being sufficiently smooth. Consequently, a fixed-wing UAV formation path planning method based on piecewise potential field (PPF) is proposed, where the problem of UAV formation flight path planning in different states can be solved by suitable design of the PPF function. Firstly, the potential field vector can be used to represent the potential field functions of obstacles and target points to meet the kinematic constraints of the UAV. Secondly, the local minimum region can be detected, the additional potential field vector being set to break away from this region. Finally, the change rules of the potential field vector of a UAV in the formation reconstruction scene can be designed, a smooth formation flight track being assured by adjusting the corresponding speed of each UAV track point. Considering the path planning of a five-UAV formation as an example, we conducted simulation experiments. The results showed that—compared with the existing methods based on APF—the results obtained using the PPF-based method considered the initial heading limits of the UAVs, the planned path being considerably smoother. Moreover, the proposed method could plan multiple UAV tracks, satisfying the known constraints without conflict in complex scenarios.

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“…Feng et al [25] proposed a modified artificial potential field to predict the obstacles' positions and solve local oscillations or avoid local minima simultaneously. Additionally, Wang et al [26] predicted the late motion state of the target according to the target position and combined the radar feedback data and state for dynamic path planning. These schemes improved UAV intelligence and allowed long-term tracking.…”

Section: Related Workmentioning

confidence: 99%

Multi-UAV Path Planning Algorithm Based on BINN-HHO

Zhang

Ding

et al. 2022

Sensors

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Multi-UAV (multiple unmanned aerial vehicles) flying in three-dimensional (3D) mountain environments suffer from low stability, long-planned path, and low dynamic obstacle avoidance efficiency. Spurred by these constraints, this paper proposes a multi-UAV path planning algorithm that consists of a bioinspired neural network and improved Harris hawks optimization with a periodic energy decline regulation mechanism (BINN-HHO) to solve the multi-UAV path planning problem in a 3D space. Specifically, in the procession of global path planning, an energy cycle decline mechanism is introduced into HHO and embed it into the energy function, which balances the algorithm’s multi-round dynamic iteration between global exploration and local search. Additionally, when the onboard sensors detect a dynamic obstacle during the flight, the improved BINN algorithm conducts a local path replanning for dynamic obstacle avoidance. Once the dynamic obstacles in the sensor detection area disappear, the local path planning is completed, and the UAV returns to the trajectory determined by the global planning. The simulation results show that the proposed Harris hawks algorithm has apparent superiorities in path planning and dynamic obstacle avoidance efficiency compared with the basic Harris hawks optimization, particle swarm optimization (PSO), and the sparrow search algorithm (SSA).

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UAV autonomous path optimization simulation based on radar tracking prediction

Cited by 11 publications

References 20 publications

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Piecewise-potential-field-based path planning method for fixed-wing UAV formation

Multi-UAV Path Planning Algorithm Based on BINN-HHO

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