Recent Advancements in Unmanned Aerial Vehicles
A project collection of Applied Sciences (ISSN 2076-3417). This project collection belongs to the section "Aerospace Science and Engineering".
Papers displayed on this page all arise from the same project. Editorial decisions were made independently of project staff and handled by the Editor-in-Chief or qualified Editorial Board members.
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Editors
Dr. Sylvain Bertrand
Dr. Sylvain Bertrand
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Guest Editor
Information Processing and Systems Department, ONERA – Paris-Saclay University, 91123 Palaiseau, France
Interests: unmanned aerial vehicles; autonomous and multi-agent systems; control systems; probabilistic risk assessment; applications to robotic and aerospace systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hyo-Sang Shin
Prof. Dr. Hyo-Sang Shin
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Guest Editor
Autonomous and Intelligent Systems Group, Centre for Autonomous and Cyberphysical Systems, Cranfield University, Cranfield MK43 0AL, UK
Interests: unmanned aerial vehicles; decision making on multi-agent systems; distributed sensing and estimation; data-centric guidance and control
Special Issues, Collections and Topics in MDPI journals
Project Overview
Dear Colleagues,
Unmanned aerial vehicles (UAVs) are now recognized as very useful tools to replace, help or assist humans in tasks such as inspection and monitoring, surveillance, search and rescue, exploration, logistics and transportation, etc. Practical uses for such missions in both civilian and defense contexts have experienced significant growth thanks to recent technological progresses. Nevertheless, some challenges and open issues remain to ensure full operational uses of UAVs.
This Special Issue aims to present recent advances in technologies and algorithms to improve the autonomy, reliability and safety of UAVs. Topics of interest include but are not limited to: advanced guidance, navigation and control algorithms; autonomy and decision making; perception and multi-sensor fusion for robust navigation; networked swarms; unmanned aerial system traffic management (UTM); new concepts and designs of vehicles; smart sensors for UAVs; new applications and field experiments; reliability, safety and risk assessment.
Dr. Sylvain Bertrand
Prof. Dr. Hyo-sang Shin
Guest Editors
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Keywords
- unmanned aerial vehicles
- unmanned aerial system traffic management
- guidance, navigation and control
- autonomy, perception, decision making
- multiple agent systems
- networked swarms
- reliability, safety, risk assessment
Published Papers (2 papers)
Open AccessArticle
A Two-Stage Co-Evolution Multi-Objective Evolutionary Algorithm for UAV Trajectory Planning
by
Gang Huang, Min Hu, Xueying Yang, Yijun Wang and Peng Lin
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Abstract
With the increasing complexity of unmanned aerial vehicle (UAV) missions, single-objective optimization for UAV trajectory planning proves inadequate in handling multiple conflicting objectives. There is a notable absence of research on multi-objective optimization for UAV trajectory planning. This study introduces a novel two-stage
[...] Read more.
With the increasing complexity of unmanned aerial vehicle (UAV) missions, single-objective optimization for UAV trajectory planning proves inadequate in handling multiple conflicting objectives. There is a notable absence of research on multi-objective optimization for UAV trajectory planning. This study introduces a novel two-stage co-evolutionary multi-objective evolutionary algorithm for UAV trajectory planning (TSCEA). Firstly, two primary optimization objectives were defined: minimizing total UAV flight distance and obstacle threats. Five constraints were defined: safe distances between UAV trajectory and obstacles, maximum flight altitude, speed, flight slope, and flight corner limitations. In order to effectively cope with UAV constraints on object space limitations, the evolution of the TSCEA algorithm is divided into an exploration phase and an exploitation phase. The exploration phase employs a two-population strategy where the main population ignores UAV constraints while an auxiliary population treats them as an additional objective. This approach enhances the algorithm’s ability to explore constrained solutions. In contrast, the exploitation phase aims to converge towards the Pareto frontier by leveraging effective population information, resulting in multiple sets of key UAV trajectory points. Three experimental scenarios were designed to validate the effectiveness of TSCEA. Results demonstrate that the proposed algorithm not only successfully navigates UAVs around obstacles but also generates multiple sets of Pareto-optimal solutions that are well-distributed across objectives. Therefore, compared to single-objective optimization, TSCEA integrates the UAV mathematical model comprehensively and delivers multiple high-quality, non-dominated trajectory planning solutions.
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Open AccessArticle
Structured Urban Airspace Capacity Analysis: Four Drone Delivery Cases
by
Sangjun Bae, Hyo-Sang Shin and Antonios Tsourdos
Cited by 2 | Viewed by 2271
Abstract
A route network-based urban airspace is one of the initial operational concepts of managing the high-density very low-level (VLL) urban airspace for unmanned aircraft system (UAS) traffic management (UTM). For the conceptual urban airspace, it is necessary to perform a quantitative analysis of
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A route network-based urban airspace is one of the initial operational concepts of managing the high-density very low-level (VLL) urban airspace for unmanned aircraft system (UAS) traffic management (UTM). For the conceptual urban airspace, it is necessary to perform a quantitative analysis of urban airspace to stakeholders for designing rules and regulations. This study aims to discuss the urban airspace capacity for four different operation types by applying different sequencing algorithms and comparing its results to provide insight and suggestions for different operation cases to assist airspace designers, regulators, and policymakers. Four drone delivery operation types that can be applied in the high-density VLL urban airspace are analysed using the suggested four metrics: total flight time; total flight distance; mission completion time; the number of conflicts. The metrics can be calculated from a flight planning algorithm that we proposed in our previous studies. The algorithm for multiple agents flight planning problems consists of an inner loop algorithm, which calculates each agent’s flight plan, and an outer loop algorithm, which determines the arrival and departure sequences. For each operation type, we apply two different outer loops with the same inner loop to suggest an appropriate sequencing algorithm. Numerical simulation results show tendencies for each type of operation with regard to the outer loop algorithms and the number of agents, and we analyse the results in terms of airspace capacity, which could be utilised for designing structures depending on urban airspace situations and environments. We expect that this study could give some intuition and support to policymakers, urban airspace designers, and regulators.
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