Modeling UAV swarm flight trajectories using Rapidly-exploring Random Tree algorithm

Kelner, Jan M.; Burzynski, Wojciech; Stecz, Wojciech

doi:10.1016/j.jksuci.2023.101909

Cited by 4 publications

(4 citation statements)

References 30 publications

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“…The other matrices in (7) denote the following: A(q) is a constraint matrix, B is a friction coefficient matrix, and E is a matrix related to the wheel torques:…”

Section: M(q)mentioning

confidence: 99%

“…Airport UGVs should continuously ensure a precise assessment of the load-bearing capacity of natural airport surfaces in a continuous way. This specific type of surface occurs at airports and airstrips for private, civil, and military use [6,7]. Routine inspection and maintenance of pavement surfaces are of the utmost importance, especially for runway strips designed for aircraft takeoff and landing at significantly higher speeds.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

BIZON–UGV for Airport Pavement Testing: Mechanics and Control

Chodnicki,

Nowakowski,

Pietruszewski

et al. 2024

Applied Sciences

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The paper presents a study of the performance and development of unmanned ground vehicles (UGVs), establishing mathematical and numerical models of the chassis system. The model analysis is performed by 3D software package SolidWorks 2018 with finite element discretization. The mesh modelling and analysis are focused on studying the strength and stiffness of the robotic platform chassis and the distribution of stress and deformation in the extremal condition. The paper also presents an autopilot design with a new cascade control system for the autonomous motion of an unmanned ground vehicle based on proportional–integral–derivative (PID) and feedforward (FF) control. The PID-FF controller is part of a UGV used in a hybrid control system for precise control and stabilization, which is necessary to increase the vehicle motion stability and maneuver precision. The hybrid PID-FF control system proposed for the ground vehicle model gives satisfactory control quality while maintaining the simplicity of the control system. The presented tests performed in mechanical design and control analysis give good results and prove the usefulness of the designed unmanned device.

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“…The other matrices in (7) denote the following: A(q) is a constraint matrix, B is a friction coefficient matrix, and E is a matrix related to the wheel torques:…”

Section: M(q)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BIZON–UGV for Airport Pavement Testing: Mechanics and Control

Chodnicki,

Nowakowski,

Pietruszewski

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Therefore, the problem of UAV path planning can be regarded as a complex optimization problem that requires effective algorithms to solve. For the problem of UAV path planning, researchers have proposed many methods to solve it, such as traditional methods such as the artificial potential field algorithm [26], Rapidly-exploring Random Tree (RRT) [27], and neural network algorithms [28], as well as emerging reinforcement learning algorithms such as the Q-learning algorithm [29]. However, these methods require a large amount of computing time and resources.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Jiang,

Yue,

Chen

et al. 2024

Biomimetics

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The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

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“…This is a result of restrictions in both the UAV and the outside environment. For example, Kelner et al [26] used the rapidly exploring random tree algorithm to solve UAV path planning problems. They treated the flight path's beginning as the search tree's root node and chose nearby nodes that met both constraint limits and had the lowest possible cost to include in the search tree.…”

Section: Relate Workmentioning

confidence: 99%

Path Planning of Unmanned Aerial Vehicles Based on an Improved Bio-Inspired Tuna Swarm Optimization Algorithm

Wang,

Xu,

2024

Biomimetics

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The Sine–Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It is presented as a solution to the shortcomings of the tuna swarm optimization (TSO) algorithm, which include its tendency to reach local optima and limited capacity to search worldwide. This algorithm updates locations using the Levy flight technique and greedy approach and generates initial solutions using an elite reverse learning process. Additionally, it offers an individual location optimization method called golden sine, which enhances the algorithm’s capacity to explore widely and steer clear of local optima. To plan UAV flight paths safely and effectively in complex obstacle environments, the SLTSO algorithm considers constraints such as geographic and airspace obstacles, along with performance metrics like flight environment, flight space, flight distance, angle, altitude, and threat levels. The effectiveness of the algorithm is verified by simulation and the creation of a path planning model. Experimental results show that the SLTSO algorithm displays faster convergence rates, better optimization precision, shorter and smoother paths, and concomitant reduction in energy usage. A drone can now map its route far more effectively thanks to these improvements. Consequently, the proposed SLTSO algorithm demonstrates both efficacy and superiority in UAV route planning applications.

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Modeling UAV swarm flight trajectories using Rapidly-exploring Random Tree algorithm

Cited by 4 publications

References 30 publications

BIZON–UGV for Airport Pavement Testing: Mechanics and Control

BIZON–UGV for Airport Pavement Testing: Mechanics and Control

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Path Planning of Unmanned Aerial Vehicles Based on an Improved Bio-Inspired Tuna Swarm Optimization Algorithm

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