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Drones
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Advances in Cartography, Mission Planning, Path Search, and Path Following...
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Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Design and Development".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 3875

Special Issue Editors

Prof. Dr. Adrian Marius Deaconu

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Faculty of Mathematics and Computer Science, Transilvania University of Brasov, 50003 Brasov, Romania
Interests: algorithms; optimization; network flow; DTN-based algorithms for UAVs; methods for map building
Special Issues, Collections and Topics in MDPI journals
Dr. Razvan Udroiu

E-Mail Website
Guest Editor
Department of Manufacturing Engineering, Transilvania University of Brasov, 29 Eroilor Boulevard, 500036 Brasov, Romania
Interests: aerospace engineering; additive manufacturing; 3D printing; composite materials
Special Issues, Collections and Topics in MDPI journals
Dr. Delia Elena Spridon

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Transilvania University of Brasov, Brasov, Romania
Interests: algorithms; parallel programming; methods for map building

Special Issue Information

Dear Colleagues,

The rapid advancement and proliferation of drone technology have ushered in a new era of possibilities and challenges in fields such as cartography, surveillance, delivery services, environmental monitoring, and agriculture. The development of sophisticated algorithms and systems for mission planning, including path search, path planning, and path following, will help us to maximize drones’ potential.

This Special Issue seeks to showcase the latest innovations in these areas, providing insights into the future of drone operations and their potential impact on society.

We are seeking original, unpublished manuscripts that are not under consideration for publication elsewhere. Submissions should clearly articulate the novelty of the research, its practical implications, and how it advances the field of drone navigation and mission planning. All accepted manuscripts will undergo a rigorous peer-review process.

The primary objective of this Special Issue is to highlight cutting-edge research and developments that address the complexities of drone navigation and mission execution in diverse environments. It will gather contributions from academia, industry, and government agencies, fostering a multidisciplinary dialogue on improving drone efficiency, effectiveness, and safety.

We are particularly interested in manuscripts that draw connections between the following topics:

  • The cartography of terrain, geomagnetic fields, lapse rates, pollution, agriculture, archaeological features, weather (e.g. temperature, pressure, wind), etc.
  • Sensor fusion for advanced navigation and positioning of drones, e.g., Kalman filters, machine learning.
  • Data acquisition by drones.
  • Collaborative drones that facilitate faster and more accurate task completion.
  • Advanced communication and data transfer between drones and bases.
  • Machine learning in pathfinding and mission accomplishment.
  • Precision agriculture, infrastructure inspection, and urban planning.
  • Advanced algorithms for path planning, mission planning, path search, and path following.
  • Drones in emergency response scenarios.
  • Drones and Internet of things.
  • Advanced drone package-delivery systems.
  • Collision avoidance and safety.

We look forward to receiving your original research articles and reviews.

Dr. Adrian Deaconu
Dr. Razvan Udroiu
Dr. Delia Elena Spridon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drone
  • UAV
  • cartography
  • path following
  • mission planning
  • machine learning
  • sensor fusion
  • data acquisition

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

23 pages, 15418 KiB  
Article
Efficient UAV Exploration for Large-Scale 3D Environments Using Low-Memory Map
by Junlong Huang, Zhengping Fan, Zhewen Yan, Peiming Duan, Ruidong Mei and Hui Cheng
Drones 2024, 8(9), 443; https://doi.org/10.3390/drones8090443 - 29 Aug 2024
Viewed by 1021
Abstract
Autonomous exploration of unknown environments is a challenging problem in robotic applications, especially in large-scale environments. As the size of the environment increases, the limited onboard resources of the robot hardly satisfy the memory overhead and computational requirements. As a result, it is [...] Read more.
Autonomous exploration of unknown environments is a challenging problem in robotic applications, especially in large-scale environments. As the size of the environment increases, the limited onboard resources of the robot hardly satisfy the memory overhead and computational requirements. As a result, it is challenging to respond quickly to the received sensor data, resulting in inefficient exploration planning. And it is difficult to comprehensively utilize the gathered environmental information for planning, leading to low-quality exploration paths. In this paper, a systematic framework tailored for unmanned aerial vehicles is proposed to autonomously explore large-scale unknown environments. To reduce memory consumption, a novel low-memory environmental representation is introduced that only maintains the information necessary for exploration. Moreover, a hierarchical exploration approach based on the proposed environmental representation is developed to allow for fast planning and efficient exploration. Extensive simulation tests demonstrate the superiority of the proposed method over current state-of-the-art methods in terms of memory consumption, computation time, and exploration efficiency. Furthermore, two real-world experiments conducted in different large-scale environments also validate the feasibility of our autonomous exploration system. Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones)
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22 pages, 4435 KiB  
Article
Research on a Distributed Cooperative Guidance Law for Obstacle Avoidance and Synchronized Arrival in UAV Swarms
by Xinyu Liu, Dongguang Li, Yue Wang, Yuming Zhang, Xing Zhuang and Hanyu Li
Drones 2024, 8(8), 352; https://doi.org/10.3390/drones8080352 - 29 Jul 2024
Cited by 2 | Viewed by 1004
Abstract
In response to the issue where the original synchronization time becomes inapplicable for UAV swarms after temporal consistency convergence due to obstacle avoidance, a new distributed consultative temporal consistency guidance law that takes into account threat avoidance has been proposed. Firstly, a six-degree-of-freedom [...] Read more.
In response to the issue where the original synchronization time becomes inapplicable for UAV swarms after temporal consistency convergence due to obstacle avoidance, a new distributed consultative temporal consistency guidance law that takes into account threat avoidance has been proposed. Firstly, a six-degree-of-freedom dynamic model and a guidance control model for unmanned aerial vehicles (UAVs) are established, and the guidance commands are decomposed into control signals for the pitch and yaw planes. Secondly, based on the theory of dynamic inversion control, a temporal consistency guidance law for a single UAV is constructed. On the other hand, an improved artificial potential field theory is used and integrated with a predictive correction network to generate guidance commands for threat avoidance. A threshold smoothing method is employed to integrate the two guidance systems, and a cluster consultation mechanism is introduced to design a two-layer temporal synchronization architecture, which negotiates to change the synchronization time of the swarm to achieve the convergence of consistency once again. Finally, in typical application scenarios, simulation verification demonstrates the effectiveness of the control method proposed in this paper. The proposed control method achieves the guidance of UAV formations to synchronize their arrival at the target location under complex threat conditions. Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones)
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20 pages, 6487 KiB  
Article
UAV Swarm Cooperative Dynamic Target Search: A MAPPO-Based Discrete Optimal Control Method
by Dexing Wei, Lun Zhang, Quan Liu, Hao Chen and Jian Huang
Drones 2024, 8(6), 214; https://doi.org/10.3390/drones8060214 - 22 May 2024
Cited by 2 | Viewed by 1381
Abstract
Unmanned aerial vehicles (UAVs) are commonly employed in pursuit and rescue missions, where the target’s trajectory is unknown. Traditional methods, such as evolutionary algorithms and ant colony optimization, can generate a search route in a given scenario. However, when the scene changes, the [...] Read more.
Unmanned aerial vehicles (UAVs) are commonly employed in pursuit and rescue missions, where the target’s trajectory is unknown. Traditional methods, such as evolutionary algorithms and ant colony optimization, can generate a search route in a given scenario. However, when the scene changes, the solution needs to be recalculated. In contrast, more advanced deep reinforcement learning methods can train an agent that can be directly applied to a similar task without recalculation. Nevertheless, there are several challenges when the agent learns how to search for unknown dynamic targets. In this search task, the rewards are random and sparse, which makes learning difficult. In addition, because of the need for the agent to adapt to various scenario settings, interactions required between the agent and the environment are more comparable to typical reinforcement learning tasks. These challenges increase the difficulty of training agents. To address these issues, we propose the OC-MAPPO method, which combines optimal control (OC) and Multi-Agent Proximal Policy Optimization (MAPPO) with GPU parallelization. The optimal control model provides the agent with continuous and stable rewards. Through parallelized models, the agent can interact with the environment and collect data more rapidly. Experimental results demonstrate that the proposed method can help the agent learn faster, and the algorithm demonstrated a 26.97% increase in the success rate compared to genetic algorithms. Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones)
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