Review on path planning algorithm for unmanned aerial vehicles

Ibrahim, Nurul Saliha Amani; Saparudin, Faiz Asraf

doi:10.11591/ijeecs.v24.i2.pp1017-1026

Cited by 8 publications

(6 citation statements)

References 41 publications

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“…However, this algorithm is not practical in a dynamic environment. To overcome this, the D* algorithm and its variants, as reviewed in [ 48 ], are efficient tools for quick re-planning in a cluttered environment. The D* algorithm updates the cost of new nodes, allowing the use of prior paths instead of re-planning the entire path.…”

Section: Literature Reviewmentioning

confidence: 99%

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

Krayani

Khan

Marchese

et al. 2023

Sensors

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Deploying unmanned aerial vehicles (UAVs) as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using traveling salesman problem with profits (TSPWP) instances executed on various training examples. This dictionary represents the world model and contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability.

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Section: Literature Reviewmentioning

confidence: 99%

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

Krayani

Khan

Marchese

et al. 2023

Sensors

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show abstract

“…In addition, this algorithm is conceptually simple and very easy to implement. Although the algorithm can achieve probabilistic completeness, it is not guaranteed to reach optimality [32]. Also, the execution time and increased computation time with an unknown convergence rate can cause problems.…”

Section: Rapid-exploring Random Treesmentioning

confidence: 99%

“…While this algorithm takes into account both static and dynamic threats, it uses an artificial field to simulate the environment for collision-free PP for the UAV. Hao and Xu [47] integrated immune network optimization with ACO to improve the shortest path finding capability of a multi-UAV system for PP [32].…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…Dewang et al [51] developed a method based on a new PSO called adaptive PSO and compared it with PSO in terms of path length and time in static settings. The method they developed reached the target in a shorter time compared to the traditional PSO method and successfully avoided obstacles [32]. Oh et al [52] have proposed a modified version of PSO for interception moving targets by adapting graph theory in their work.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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An overview of path planning technologies for unmanned aerial vehicles

Bal

2022

THERM SCI

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Unmanned aerial vehicles, due to their superior maneuverability and reduced costs can easily perform tasks that are too difficult and complex to be performed with manned aircraft, under all conditions. In order to cope with various obstacles and operate in complex and unstable environmental conditions, the unmanned aerial vehicles must first plan its path. One of the most important problems to investigated in order to find an optimal path between the starting point and the target point of the unmanned aerial vehicles is path planning and choosing the appropriate algorithm. These algorithms find the optimal and shortest path, and also provide a collision-free environment for unmanned aerial vehicles. It is important to have path planning algorithms to calculate a safe path to the final destination in the shortest possible time. However, algorithms are not guaranteed to provide full performance in each path planning situation. Also, each algorithm has some specifications, these specifications make it possible to make them suitable in complex situations. Although there are many studies in path planning literature, this subject is still an active research area considering the high maneuverability of unmanned aerial vehicles. In this study, the most used methods of graph search, sampling-based algorithms and computational intelligence-based algorithms, which have become one of the important technologies for unmanned aerial vehicles and have been the subject of extensive research, are examined and their pros and cons are emphasized. In addition, studies conducted in the field of unmanned aerial vehicles with these algorithms are also briefly mentioned.

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“…Also [15], presented the latest algorithms for path planning of UAVs in both known and unknown environments. [16,17] Summarized the better understanding on the algorithms and described briefly the advantages and disadvantages of each algorithm. Moreover, in order to determine an optimal or near-optimal path [18], provided an overview of popular techniques recommended for UAVs navigation in an environment and path planning algorithms recommended for UAVs.…”

Section: Introductionmentioning

confidence: 99%

Hybrid algorithms in path planning for autonomous navigation of unmanned aerial vehicle: a comprehensive review

Tuyet Minh,

Ba Dung

2024

Meas. Sci. Technol.

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Path planning for Unmanned Aerial Vehicle (UAV) is the process of determining the path that travels through each location of interest within a particular area. There are numerous algorithms proposed and described in the publications to address UAV path planning problems. However, in order to handle the complex and dynamic environment with different obstacles, it is critical to utilize the proper fusion algorithms in planning the UAV path. This paper reviews some hybrid algorithms used in finding the optimal route of UAVs that developed in the last ten years as well as their advantages and disadvantages. The UAV path planning methods were classified into categories of hybrid algorithms based on traditional, heuristic, machine learning approaches. Criteria used to evaluate algorithms include execution time, total cost, energy consumption, robustness, data, computation, obstacle avoidance, and environment. The results of this study provide reference resources for researchers in finding the path for UAVs.

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Review on path planning algorithm for unmanned aerial vehicles

Cited by 8 publications

References 41 publications

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

An overview of path planning technologies for unmanned aerial vehicles

Hybrid algorithms in path planning for autonomous navigation of unmanned aerial vehicle: a comprehensive review

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