Path-planning and obstacle avoidance algorithms for UAVs: a systematic literature review

Almazrouei, Khawla; Nassif, Ali Bou; Al-Shabi, Mohammad

doi:10.1117/12.2664056

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…[19] Provided an overview of UAV path planning techniques, which are divided into five primary groups: machine learning, hybrid algorithms, heuristics, meta-heuristics, and classical methods. Additionally, path planning algorithms can be classified as either heuristic and non-heuristic or accurate methods [20], can be used to group different UAV path planning strategies: cooperative, non-cooperative, and representational techniques [12], or categories into off-line and on-line methods [21]. [22] Performed a thorough and comparative analysis of current UAV path-planning algorithms for both heuristic and nonheuristic techniques.…”

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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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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Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on energy consumption, data storage capacity, and processing power, present formidable obstacles in developing collision avoidance algorithms. This review paper explores the challenges of ensuring safe drone operations, focusing on collision avoidance. We explore collision avoidance methods for UAVs from various perspectives, categorizing them into four main groups: obstacle detection and avoidance, collision avoidance algorithms, drone swarm, and path optimization. Additionally, our analysis delves into machine learning techniques, discusses metrics and simulation tools to validate collision avoidance systems, and delineates local and global algorithmic perspectives. Our evaluation reveals significant challenges in current drone collision prevention algorithms. Despite advancements, critical UAV network and communication challenges are often overlooked, prompting a reliance on simulation-based research due to cost and safety concerns. Challenges encompass precise detection of small and moving obstacles, minimizing path deviations at minimal cost, high machine learning and automation expenses, prohibitive costs of real testbeds, limited environmental comprehension, and security apprehensions. By addressing these key areas, future research can advance the field of drone collision avoidance and pave the way for safer and more efficient UAV operations.

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Parameter estimation of an unmanned aerial vehicle using dandelion algorithm

AlShabi,

Saleh,

Obaideen

et al. 2024

Unmanned Systems Technology XXVI

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In this study, we introduce a novel approach to the parameter estimation of Unmanned Aerial Vehicles (UAVs) utilizing the dandelion algorithm, a bio-inspired optimization technique that simulates the seed dispersal mechanism of dandelions. With UAVs increasingly becoming integral to various sectors, accurate parameter estimation emerges as a critical factor in ensuring their optimal performance and safety. Traditional parameter estimation methods often fall short, plagued by computational inefficiencies and a propensity for local optima, which can significantly hinder UAV operations. The dandelion algorithm, with its unique global search capabilities and adeptness in navigating multidimensional spaces, presents a solution that markedly enhances the precision and speed of parameter estimation. Through a series of simulations involving diverse UAV models, this study compares the performance of the dandelion algorithm against the conventional technique; the Particle Swarm Optimization (PSO), demonstrating its superior ability in achieving rapid convergence, higher accuracy, and an exceptional aptitude for avoiding local optima. Our findings not only underscore the algorithm's potential to revolutionize UAV parameter estimation but also highlight its applicability in advancing UAV technology and bio-inspired computational algorithms. This research contributes to the aerospace engineering field by offering an innovative, efficient alternative to existing parameter estimation methods, promising significant improvements in the design, operation, and safety of UAV systems across a spectrum of applications.

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Path-planning and obstacle avoidance algorithms for UAVs: a systematic literature review

Cited by 3 publications

References 23 publications

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

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

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Parameter estimation of an unmanned aerial vehicle using dandelion algorithm

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