RGSO-UAV: Reverse Glowworm Swarm Optimization inspired UAV path-planning in a 3D dynamic environment

Chowdhury, Aparajita; De, Debashis

doi:10.1016/j.adhoc.2022.103068

Cited by 29 publications

(14 citation statements)

References 41 publications

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“…Especially, Butterfly Optimization Algorithm (BOA) [15], Whale Optimization Algorithm (WOA) [16][17], Sparrow Search Algorithm (SSA) [18][19], Particle Swarm Optimization (PSO) [20], Ant Colony Optimization (ACO) [21] , Chimp Optimization Algorithm (COA) [22], Grey Wolf Optimizer (GWO) [23][24] [25], Reverse Glowworm Swarm Optimization (RGSO) [26], and so on. Recently, some researchers have designed some improved algorithms for UAV trajectory planning.…”

Section: Related Workmentioning

confidence: 99%

“…Assuming that the flight path of the UAV consists of n waypoints in the planning process, the distance between the 𝑖th waypoint are noted as 𝑤(𝑖) = (𝑥 𝑖 , 𝑦 𝑖 , 𝑧 𝑖 ) and the 𝑖 + 1th waypoint are noted as 𝑤(𝑖 + 1) = (𝑥 𝑖+1 , 𝑦 𝑖+1 , 𝑧 𝑖+1 ) is denoted as 𝐿 𝑖 . The planned trajectory should satisfy Equation (26).…”

Section: Objective Function and Constraint Functionmentioning

confidence: 99%

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SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

Zheng,

He,

2024

Preprint

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Unmanned Aerial Vehicle (UAV) route planning is an intricate issue that requires the comprehensive consideration of multiple factors and the combination of suitable strategies to achieve efficient and safe flight paths. Its purpose is to plan a suitable navigational path for the drone to achieve a specific task or avoid obstacles. In practical applications, UAVs are usually required to accomplish tasks in a variety of complex environments, resulting in the feasible flight paths will be reduced and path planning for UAVs becomes difficult. Accordingly, we design a simple yet useful planner, called Self-adaptive Golden Equilibrium Optimization algorithm (SGEO). The proposed algorithm combines the new self-adaptive approach, the acceleration control factor and the Golden-SA strategy in order to balance the exploitation and exploration capabilities. The novel self-adaptive strategy is used to expand the global search range of the algorithm and enhance its ability for global exploration; the acceleration control factor is introduced to control the parameters a1 and a2 and improve the convergence ability of the algorithm; the Golden-SA strategy helps the candidate particles achieve better balance between different dimensions based on the golden ratio and sine function, this enables the particles to explore the search space more comprehensively. The limitations in UAV route planning are translated into the objective function, and four more algorithms are introduced to compare with SGEO in two different circumstances. The simulation findings reveal that SGEO excels at three-dimensional path planning for UAV in complicated environments.

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Section: Related Workmentioning

confidence: 99%

Section: Objective Function and Constraint Functionmentioning

confidence: 99%

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

Zheng,

He,

2024

Preprint

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“…After the aforementioned processing, a collection of nodes serves as the UAV's working area, and the following equation can be used to translate the length of the UAV flight track into the combination of the unique points' distances [33] .…”

Section: Mountainmentioning

confidence: 99%

Particle Swarm Optimization Bat Algorithm Path Automatically Planning Research for Police Drones in Hilly Cities

XUE,

TAN,

DAI

et al. 2024

系统科学与信息学报(英文)

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Mountain cities are complex asymmetric dynamic network architectures, and the flight of UAVs in this environment is subject to various constraints, while efficiency is a crucial factor in the trajectory planning of police UAVs, which need to maintain high efficiency and safe flight paths between their starting and ending points, but the traditional trajectory planning method cannot meet the requirements of rapid maneuvering of police UAVs. To achieve this, a 3D terrain map is built, an objective function is established for the flight cost in the UAV trajectory planning process, and a planning algorithm called particle swarm optimization bat algorithm (PSOBA) is proposed. PSOBA combines the characteristics of the bat algorithm (BA) and the particle swarm optimization algorithm (PSO) to improve population diversity and resolve the delayed convergence issue in the last phases of BA. Simulation results show that PSOBA is more effective than BA, with a search time for the best solution that is approximately 20.43% shorter and a convergence value of the objective function that is approximately 38% smaller. PSOBA is also able to plan a quicker, shorter, and safer flight path compared to other trail planning algorithms that enhance the bat algorithm. These findings suggest that PSOBA is a powerful algorithm with potential application value in UAV trajectory planning control in the mobile intelligence era.Contribute to the service of public social security.

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“…The research on trajectory planning algorithms for multi-rotor UAVs has rapidly evolved in recent years.Aparajita Chowdhury and Debashis De [1] proposed a new heuristic algorithm, Reverse Firefly Swarm Optimization (RGSO) algorithm, which solves the path planning of rotorcraft UAVs in a 3D dynamic environment and greatly reduces the cost function value and flight time.Ram Kishan Dewangan et al [2] applied the Gray Wolf Optimization (GWO) algorithm to UAV 3D motion planning, which improved the UAV's path search capability and trajectory quality. Gao Yang Li et al [3] proposed a path planning method based on the all-particle push Mustang algorithm for the UAV path planning problem with large computation and difficult solution, which greatly improved the search ability of the algorithm to get the global optimal solution; Wenqian Liu et al [4] improved the algorithm to improve the directionality and purposiveness of the RRT tree expansion, and the algorithm was able to find a superior solution; Wang Wenfei et al [5] improved the dynamic window method, extended the subfunction space of trajectory evaluation, and overcame this drawback that traditional path planning algorithms are easy to fall into the local optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Autonomous planning method for intelligent UAV swarm flight path based on swarm policy

Guo,

Yin,

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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Aiming at the flight path planning problem of intelligent UAVs (swarms), this paper designs a "swarm-type" planning strategy, which improves the distributed spatio-temporal trajectory planning method under special scenarios. According to the spatial distribution of UAVs, the UAV swarm is dynamically divided into a number of groups and isolated UAVs, and a "swarm-type" coordination mechanism is established for UAV trajectory conflicts within each group, and the group planning consists of the improved Efficient Multi-Intelligent Aperture Path Finding (EMAPF) and trajectory co-optimization, which significantly improves the efficiency and safety of the planning. The group planning consists of improved efficient multi-intelligence pathfinding (EMAPF) and joint optimization of trajectories, which significantly improves the planning efficiency and safety.

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RGSO-UAV: Reverse Glowworm Swarm Optimization inspired UAV path-planning in a 3D dynamic environment

Cited by 29 publications

References 41 publications

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

Particle Swarm Optimization Bat Algorithm Path Automatically Planning Research for Police Drones in Hilly Cities

Autonomous planning method for intelligent UAV swarm flight path based on swarm policy

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