Receding-Horizon Trajectory Planning for Multiple UAVs Using Particle Swarm Optimization

Vijayakumari, Dileep M.; Kim, Taegyun; Suk, Jinyoung; Mo, Hyemin

doi:10.2514/6.2019-1165

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…There are other methods applied to path planning with UAV swarms. For example, Vijayakumari et al make use of Particle Swarm Optimization for optimal control of multiple UAVs in a decentralized way [27]. They manage to simplify the computation of the problem by means of discretization.…”

Section: Evolutionarymentioning

confidence: 99%

UAV swarm path planning with reinforcement learning for field prospecting

2022

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There has been steady growth in the adoption of Unmanned Aerial Vehicle (UAV) swarms by operators due to their time and cost benefits. However, this kind of system faces an important problem, which is the calculation of many optimal paths for each UAV. Solving this problem would allow control of many UAVs without human intervention while saving battery between recharges and performing several tasks simultaneously. The main aim is to develop a Reinforcement Learning based system capable of calculating the optimal flight path for a UAV swarm. This method stands out for its ability to learn through trial and error, allowing the model to adjust itself. The aim of these paths is to achieve full coverage of an overflight area for tasks such as field prospection, regardless of map size and the number of UAVs in the swarm. It is not necessary to establish targets or to have any previous knowledge other than the given map. Experiments have been conducted to determine whether it is optimal to establish a single control for all UAVs in the swarm or a control for each UAV. The results show that it is better to use one control for all UAVs because of the shorter flight time. In addition, the flight time is greatly affected by the size of the map. The results give starting points for future research, such as finding the optimal map size for each situation.

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Section: Evolutionarymentioning

confidence: 99%

UAV swarm path planning with reinforcement learning for field prospecting

2022

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“…Control optimization research focuses on evolutionary algorithms [13] including genetic algorithms [14,15] and particle swarm optimization [16,17]. Some papers show implementation of particle swarm optimization for single-quadcopter controllers [18][19][20], UAV formations [21][22][23], UAV trajectory optimization [24], UAV movement planning [25] and UAV formations [26] in an uncertain environment. However, swarm optimization for vector field-controlled UAV formations remains under-researched.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization for Target Encirclement by a UAV Formation

Muslimov

2023

Intels’22

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“…Among them, the latter refers to the implementation of point-to-point routes in a known environment. Trajectory planning usually combines the dynamic model [7] with the motion planning to solve the overall optimal control issue of the moving body, which leads to other problems, e.g., high complexity, complicated operation, and low computational efficiency, greatly increasing the difficulty of its application [8]. At present, the track planning algorithms are mainly divided into four cate-gories: (i) the schematic method consisting of the general view method, the Voronoi diagram method, and the contour method [9][10][11]; (ii) the unit decomposition plan composed of the grid and the quadtree method [12]; (iii) the artificial potential field method consisting of the wave propagation method and the harmonic function method [13,14]; (iv) the heuristic planning method based on the genetic algorithm and the neural network algorithm [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Trajectory Planning and Control for Constrained UAV Based on Differential Flatness

Zhang

Liu

et al. 2022

International Journal of Aerospace Engineering

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The trajectory planning of UAV with nonholonomic constraints is usually taken as differential algebraic equation to solve the optimal control problem of functional extremum under the condition of inequality constraints. However, it can be challenging to meet the requirements of real-time for the high complexity. A differential flat theory based on B-spline trajectory planning can replace the optimal control problem with nonlinear programming and be a good means to achieve the efficient trajectory planning of an UAV under multiple dynamic constraints. This research verifies the feasibility of this theory with actual flight experiments.

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Receding-Horizon Trajectory Planning for Multiple UAVs Using Particle Swarm Optimization

Cited by 10 publications

References 21 publications

UAV swarm path planning with reinforcement learning for field prospecting

UAV swarm path planning with reinforcement learning for field prospecting

Particle Swarm Optimization for Target Encirclement by a UAV Formation

Real-Time Trajectory Planning and Control for Constrained UAV Based on Differential Flatness

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