Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

Zhang, Changchun; Liu, Yifan; Hu, Chunhe

doi:10.3390/biomimetics7040225

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…When modeling grey wolf populations in this study, the populations are classified into four types: α, δ, and ω. The best solution is referred to as α wolves, the second and third solutions as β and δ wolves, and the remaining solutions as ω [36]. When solving problems such as engineering applications, because the traditional GWO is randomly initialized with wolf positions, which primarily affects the algorithm's search efficiency, the initialized populations must be distributed as evenly as possible in the initial space, and chaotic mapping has traversal, randomness, and other characteristics that can effectively improve the algorithm's global searchability [37].…”

Section: Grey Wolf Population Initializationmentioning

confidence: 99%

Hybrid Optimization Algorithm for solving Path Planning Problems Based on Grey Wolf Optimization Algorithm

Cheng,

Liu

2024

Preprint

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The grey wolf optimization algorithm is a heuristic optimization algorithm based on the behavior of grey wolf groups in nature. It has the advantages of a simple concept and few adjustment parameters, and it is widely used in a variety of fields. To address the above shortcomings, this study proposes an improved grey wolf optimization algorithm that uses the gold migration formula from the gold mining optimization algorithm and incorporates chaotic mapping, the gold mining optimization algorithm, the vertical and horizontal crossover strategy, and the Gaussian mutation. Chaos mapping is used to initialize the grey wolf population, ensuring that it is more evenly distributed across the search space. The grey wolf algorithm's α-wolf is updated with the gold migration formula from the gold mining optimization algorithm, increasing its diversity. Horizontal crossover is used for searching, which reduces the algorithm's blind zone and improves its global search capability. Vertical crossover prevents the algorithm from converging prematurely. The introduction of the Gaussian mutation effectively prevents the algorithm from falling into the local optimum premature problem. To determine the algorithm's effectiveness, this study compares the improved Grey Wolf optimization algorithm to other Grey Wolf optimization algorithms on 23 benchmark functions. After experimental verification, the proposed algorithm outperforms the other comparative algorithms. Meanwhile, when the algorithm is applied to path planning, the paths generated are shorter, and the running time is shorter than that of other algorithms, demonstrating the algorithm's applicability.

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Section: Grey Wolf Population Initializationmentioning

confidence: 99%

Hybrid Optimization Algorithm for solving Path Planning Problems Based on Grey Wolf Optimization Algorithm

Cheng,

Liu

2024

Preprint

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“…Although applying the basic GWO algorithm for path planning can improve the path planning ability of robotic arms to a certain extent, there are still problems such as low operating accuracy, slow convergence speed, and easy falling into local optima. Much of the literature has also proposed corresponding improvement methods for the above issues [33][34][35][36]. Although these methods can play a certain role in addressing specific problems, there are still problems such as poor robustness, poor universality, and susceptibility to local optima when applied to the path planning of robotic arms.…”

Section: Introductionmentioning

confidence: 99%

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

Feng,

Dai,

Zhou

et al. 2024

Machines

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Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.

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“…UAVs can complete low-altitude penetration in marine combat environments and successfully execute missions to guarantee the safety of sea territories. These missions can be conducted via two approaches, namely, the construction of combat environments for electronic navigational charts (ENCs) [2] and UAV path planning [3,4].…”

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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Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

Cited by 13 publications

References 31 publications

Hybrid Optimization Algorithm for solving Path Planning Problems Based on Grey Wolf Optimization Algorithm

Hybrid Optimization Algorithm for solving Path Planning Problems Based on Grey Wolf Optimization Algorithm

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

A Photosensitivity-Enhanced Plant Growth Algorithm for UAV Path Planning

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