Optimal path planning for drones based on swarm intelligence algorithm

Saeed, Rashid A.; Omri, Mohamed; Abdel‐Khalek, S.; Ali, Elmustafa Sayed; Alotaibi, Maged F.

doi:10.1007/s00521-022-06998-9

Cited by 72 publications

(22 citation statements)

References 78 publications

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“…These two-dimensional solutions provide a suitable solution mostly for agricultural problems and patrol and monitoring tasks. The following algorithms are most often used by researchers during route planning suitable for inventory: the traditional squid algorithm [10]; the genetic algorithm [11,12]; the rapidly expanding random tree algorithm [13,14]; the traditional particle swarm optimization algorithm (PSO); the ant colony algorithm [15,16]; and deep neural networks [17,18]. Most of the research only uses the shortest flight path [19] or the least energy consumption [20] to generate the singleobjective optimization model, and further uses the convex approximation strategy [21] to calculate the route.…”

Section: Literature Reviewmentioning

confidence: 99%

A Path Planning Model with a Genetic Algorithm for Stock Inventory Using a Swarm of Drones

Gubán

Udvaros

2022

Drones

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In this paper, a mathematical model and solution for performing the inventory tasks of a multi-user, mixed warehouse in which neither satellite positioning nor other IT solutions can be used was presented. After reviewing the literature on road planning and the use of drones in warehouses, a method is presented that can be used to control drones that can be moved in all directions for imaging and transmission. The proposed method consists of three main steps. As a first step, we provide the mathematical model and solution method needed to determine the (optimal execution time) access routes required for processing the compartments of the warehouses. This is an initial step before starting the inventory. This considers the structure of the warehouse, its features, the number of drones, and the parameters of the drones. In the second step, based on the routes obtained in the first step, the real-time movement of the drones was controlled during processing, including camera movement and image recording. The third step is post-processing, i.e., processing the images for QR code identification, interpreting the QR code, and recognizing empty compartments for inventory control. A major advantage for users of the solution method is that the result can be achieved automatically without an external orientation device, relying solely on its own movement and the organization of a pre-planned route. The proposed model and solution method are suitable not only for inventory control, but also for solving other problems matching the model.

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Section: Literature Reviewmentioning

confidence: 99%

A Path Planning Model with a Genetic Algorithm for Stock Inventory Using a Swarm of Drones

Gubán

Udvaros

2022

Drones

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“…Several significant studies have been conducted in order to apply data-driven machine-learning algorithms for detecting faults and anomalies in aerial vehicles. Some researchers combined chaotic dynamics with powerful swarm-based algorithms used in mobility models such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) [27], [28], [29]. The Lorenz and Rössler attractors are time-discretized, and the three-dimensional chaotic maps are addressed via a threedimensional solvable chaos graph built from general chaos solutions.…”

Section: Related Workmentioning

confidence: 99%

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

NEBE,

SANNI,

ADETONA

et al. 2022

IJACSA

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Most existing identification and tackling of chaos in swarm drone missions focus on single drone scenarios. There is a need to assess the status of a system with multiple drones, hence, this research presents an on-the-fly chaotic behavior detection model for large numbers of flying drones using machine learning techniques. A succession of three Artificial Intelligence knowledge discovery procedures, Logistic Regression (LR), Convolutional Neural Network (CNN), Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) were employed to reduce the dimension of the actual data of the swarm of drone's flight and classify it as non-chaotic and chaotic. A onedimensional, multi-layer perceptive, deep neural network-based classification system was also used to collect the related characteristics and distinguish between chaotic and non-chaotic conditions. The Rössler system was then employed to deal with such chaotic conditions. Validation of the proposed chaotic detection and mitigation technique was performed using realworld flight test data, demonstrating its viability for real-time implementation. The results demonstrated that swarm mobility horizon-based monitoring is a viable solution for real-time monitoring of a system's chaos with a significantly reduced commotion effect. The proposed technique has been tested to improve the performance of fully autonomous drone swarm flights.

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“…3D path planning algorithm taxonomy based on deep learning shows superior performance, more powerful tools, and semantic features. Many path-determining algorithms have emerged, the most important of which are natural algorithms, the most famous of which are Ant colony optimization (ACO) and bee colony optimization (ABC) [11,12].…”

Section: Related Workmentioning

confidence: 99%

Obstacle Avoidance and Path Planning for UAV Using Laguerre Polynomial

2022

IJIES

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Recently, path planning algorithms have been one of the primary and important functions of unmanned aerial vehicles (UAVs). Path planning algorithms in UAVs focused on path length, average path length, computation time, and standard deviation from the mean path length. In spite of this, it faced many difficulties and problems, such as many obstacles, path segmentation, and the increasing number of obstacles and paths in urban environments. This work proposes polynomial functions for path planning and obstacle avoidance. Since it enables us to plan the path in static internal environments, it enables us to plan the path quickly and with less computing time because it does not require high memory and does not require pre-compute of the path. Instead, the route is plotted in real time, Where the appropriate equation is entered into the program, so that the vehicle follows the curve of the entered equation. An accurate data set and metrics were used to measure the efficiency of the proposed method. The experimental results showed a clear improvement in the work of the polynomial function on A*, PSO and genetic algorithms, as this improvement appears very clearly when compared to the computing time, which was reduced by 15% in the method of polynomial functions where the path calculation took only parts of The second, as well as the path length was halved in the polynomial method as the results showed, which reduces the time of battery and memory consumption, the cost of calculating the path and the time to reach the goal.

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Optimal path planning for drones based on swarm intelligence algorithm

Cited by 72 publications

References 78 publications

A Path Planning Model with a Genetic Algorithm for Stock Inventory Using a Swarm of Drones

A Path Planning Model with a Genetic Algorithm for Stock Inventory Using a Swarm of Drones

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

Obstacle Avoidance and Path Planning for UAV Using Laguerre Polynomial

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