A Time-Varying Adaptive Inertia Weight based Modified PSO Algorithm for UAV Path Planning

Fan, Mingyu; Akhter, Yasmin

doi:10.1109/icrest51555.2021.9331101

Cited by 32 publications

(17 citation statements)

References 11 publications

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“…This concept of modification and improvement upon a core algorithm is also demonstrated in [78], three refined PSO algorithms are created, a maximum density convergence DPSO algorithm (MDC-DPSO), a fast cross-over DPSO algorithm (FCO-DPSO), and an accurate coverage exploration DPSO algorithm (ACE-DPSO), each proposing to fill a corresponding defined need within the reconnaissance problem the authors seek to solve. The extensiblity of the PSO algorithm is also demonstrated in [61] extending previous work on time-varying inertia weight and adaptive inertia weight approaches implementing a multi-fusion, adaptive inertia weight approach. Whilst the fundamental core ACO and PSO principles remain in these implementations, highlighted is the significant potential that exists for refinement and explorative evaluation of differing control parameters within the core algorithm concepts itself, together with the exploitation of combinatorial solution approaches that can be taken.…”

Section: Uav Path-planning Approachesmentioning

confidence: 57%

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

2023

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Unmanned aerial vehicles (UAVs) have the potential to make a significant impact in a range of scenarios where it is too risky or too costly to rely on human labour. Fleets of autonomous UAVs, which complete tasks collaboratively while managing their basic flight and related tasks independently, present further opportunities along with research and regulatory challenges. Improvements in UAV construction and components, along with developments in embedded computing hardware, communication mechanisms and sensors which may be mounted on-board a UAV are nearing the point where commercial deployment of fleets of autonomous UAVs will be technically possible. To fulfil this potential, UAVs will need to operate safely and reliably in complex and potentially dynamically changing environments with path-planning, obstacle sensing and collision avoidance paramount. This survey presents an original environment complexity classification, and critically analyses the current state of the art in relation to UAV path-planning approaches. Moreover, it highlights the existing challenges in environment complexity modelling and representation, path-planning approaches, and outlines open research questions together with future directions.

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Section: Uav Path-planning Approachesmentioning

confidence: 57%

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

2023

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“…This 3D deployment and resource utilization has been addressed by powerful optimization methods such as convex optimization [44] or game theory [45]. In addition, various path-planning methods have been deployed for obtaining optimal paths, including graph-based methods such as the A* algorithm [46,47], or evolutionary methods such as the Particle Swarm Optimization (PSO) [48,49]. The observation that UAS navigation can be treated as a sequential decision-making problem has led more and more researchers to the use of learning-based methods for solving complex navigation problems and intelligently managing onboard resources [50].…”

Section: Autonomous Navigationmentioning

confidence: 99%

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Politi,

Purucker,

Larsen

et al. 2024

Electronics

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Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed.

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“…To avoid the PSO local minimum, a time-varying adaptive inertia weight called NPSO was proposed [ 59 ] for the PSO, which significantly improves the generation of an optimal UAV path. To address the shortcomings of PSO algorithms, this study improves the weight and learning factor of the particle swarms [ 60 ].…”

Section: Bio-inspired Algorithms For Uav Path Planningmentioning

confidence: 99%

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

Poudel

Arafat

Moh

2023

Sensors

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Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.

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A Time-Varying Adaptive Inertia Weight based Modified PSO Algorithm for UAV Path Planning

Cited by 32 publications

References 11 publications

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

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