Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
10.0
5.2
Journals
Drones
Special Issues
Advances in Cartography, Mission Planning, Path Search, and Path Following...
share announcement

Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones: 2nd Edition

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (20 April 2026) | Viewed by 6624

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: additive manufacturing; polymer and composite materials; CAD/CAM technologies; advanced manufacturing processes; quality analysis of additively manufactured parts; surface quality and performance of polymer components; rapid prototyping technologies
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 Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of Drones on "Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones: 2nd Edition".

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We are seeking original, unpublished manuscripts that are not under consideration for publication elsewhere.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 Marius 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

36 pages, 24670 KB  
Article
A High- and Low-Level Decoupled Reinforcement Learning Method for Multi-UAV Cooperative Search
by Jianjie Qiu, Yichao Cai, Hao Li, Lei Ni, Kai Yuan and Siyuan Cui
Drones 2026, 10(7), 483; https://doi.org/10.3390/drones10070483 - 24 Jun 2026
Viewed by 284
Abstract
Multi-UAV cooperative search with static unknown targets requires both efficient regional allocation and responsive local maneuvering. However, single-level learning methods often suffer from redundant coverage, unclear division of labor, and unstable training. This paper proposes a high- and low-level decoupled reinforcement learning method [...] Read more.
Multi-UAV cooperative search with static unknown targets requires both efficient regional allocation and responsive local maneuvering. However, single-level learning methods often suffer from redundant coverage, unclear division of labor, and unstable training. This paper proposes a high- and low-level decoupled reinforcement learning method for multi-UAV cooperative search. The high level periodically generates UAV-specific regional goals from visitation maps, target-existence belief maps, and UAV positions, while a spatial self-attention module enhances the representation of unvisited regions, high-belief target areas, and UAV distributions. The low level performs discrete steering actions based on local observations and high-level contexts, supported by a structured reward that encourages coverage, target discovery, goal-oriented progress, repeated-visit suppression, and boundary-safe motion. Simulation experiments are conducted in a two-dimensional grid environment with static targets and ideal sensing. Under this simplified simulation setting, the proposed method achieves higher training return and coverage rate than representative baseline algorithms while maintaining a high final target discovery rate and reaching the discovery threshold earlier. Ablation and visualization results further demonstrate the effectiveness and interpretability of the proposed hierarchical guidance mechanism within the considered simulation scenario. Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones: 2nd Edition)
►▼ Show Figures

Figure 1

49 pages, 37730 KB  
Article
Comparative Evaluation of Classical, Hybrid, and RL-Based 3D Trajectory Planning for Multi-UAV Systems
by Ilya Mashkov, Angelika Kochetkova, Valerii Serpiva, Grigoriy Yashin and Pavel Golikov
Drones 2026, 10(6), 452; https://doi.org/10.3390/drones10060452 - 9 Jun 2026
Viewed by 653
Abstract
This study investigates offline trajectory planning strategies for multi-UAV missions in complex 3D environments, with the aim of systematically comparing classical, hybrid, and reinforcement learning-based approaches under unified evaluation conditions. Two simulation scenarios were considered: an uneven terrain environment with elevation-induced constraints and [...] Read more.
This study investigates offline trajectory planning strategies for multi-UAV missions in complex 3D environments, with the aim of systematically comparing classical, hybrid, and reinforcement learning-based approaches under unified evaluation conditions. Two simulation scenarios were considered: an uneven terrain environment with elevation-induced constraints and a planar obstacle-rich environment. The evaluated planners include graph-based (A*), sampling-based (RRT, RRT*), gradient-based (APF), a hybrid APF B-RRT* method, and a DQN-based reinforcement learning planner with spatial attention and reward shaping. Performance was assessed using geometric, safety, energetic, and computational metrics. The results show that A* consistently produces the shortest and most stable trajectories with low energy consumption but at increased computational cost in high-resolution environments. Sampling-based planners exhibit higher variability and planning time, while APF achieves computational efficiency but may violate safety margins. The hybrid planner provides improved robustness across scenarios. The reinforcement learning planner demonstrates consistent safety compliance and strong inter-UAV separation in both environments, also with longer trajectories and higher energy usage. Overall, the study highlights trade-offs between determinism, scalability, safety, and adaptability across planning paradigms. Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones: 2nd Edition)
►▼ Show Figures

Figure 1

28 pages, 8016 KB  
Article
Dynamic Real-Time Multi-UAV Cooperative Mission Planning Method Under Multiple Constraints
by Chenglou Liu, Yufeng Lu, Fangfang Xie, Tingwei Ji and Yao Zheng
Drones 2026, 10(2), 132; https://doi.org/10.3390/drones10020132 - 14 Feb 2026
Cited by 4 | Viewed by 1557
Abstract
As UAV popularity soars, so does the mission planning associated with it. Classical planning approaches suffer from the triple problems of decoupling of task assignment and path planning, poor real-time and scalability, and limited adaptability. Aiming at these challenges, this paper proposes a [...] Read more.
As UAV popularity soars, so does the mission planning associated with it. Classical planning approaches suffer from the triple problems of decoupling of task assignment and path planning, poor real-time and scalability, and limited adaptability. Aiming at these challenges, this paper proposes a multi-UAV real-time collaborative mission planning method based on UAV states. First, the employed Dubins path accurately represents the distance between tasks and satisfies curvature constraints without smoothing, thus achieving a coupled solution for task assignment and path planning. Then, a series of acceleration techniques are applied to guarantee the real-time performance of the method, including task clustering to reduce the decision space, allocation strategies with fewer iterations, and efficient distance cost calculation methods. To enhance robustness and adaptability, real-time assignment of new tasks and task reassignment due to the reduction of available UAVs are appropriately handled. Finally, simulations highlight that the proposed method only increases the path length by 9.57% compared to benchmark method, while achieving a 4–5 orders-of-magnitude improvement in planning speed, with a single mission planning of about 0.0003 s. Moreover, it easily scales to large-scale scenarios (0.0029 s, with 1000 UAVs and 25,000 tasks). Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones: 2nd Edition)
►▼ Show Figures

Figure 1

15 pages, 2681 KB  
Article
Strategic Vertical Port Placement and Routing of Unmanned Aerial Vehicles for Automated Defibrillator Delivery in Mountainous Areas
by Abraham Mejia-Aguilar, Giacomo Strapazzon, Eliezer Fajardo-Figueroa and Michiel J. van Veelen
Drones 2026, 10(1), 38; https://doi.org/10.3390/drones10010038 - 7 Jan 2026
Cited by 1 | Viewed by 1565
Abstract
Out-of-hospital cardiac arrest (OHCA) is a major cause of death during mountain activities in the Alpine regions. Due to the time-critical nature of these emergencies and the logistical challenges of remote terrain, emergency medical services (EMS) are investigating the use of unmanned aerial [...] Read more.
Out-of-hospital cardiac arrest (OHCA) is a major cause of death during mountain activities in the Alpine regions. Due to the time-critical nature of these emergencies and the logistical challenges of remote terrain, emergency medical services (EMS) are investigating the use of unmanned aerial vehicles (UAVs) to deliver automated external defibrillators (AEDs). This study presents a geospatial strategy for optimising AED delivery by UAVs in mountainous environments, using the Province of South Tyrol, Italy, as a model region. A Geographic Information System (GIS) framework was developed to identify suitable sites for vertical drone ports based on terrain, infrastructure, and regulatory constraints. A Low-Altitude-Flight Elevation Model (LAFEM) was implemented to generate obstacle-avoiding, regulation-compliant 3D flight paths using least-cost path analysis. The results identified 542 potential vertical-port locations, covering approximately 49% of South Tyrol within ten minutes of flight, and demonstrated significant time savings for AED delivery in field tests compared with manual and Euclidean routing. These findings show that integrating GIS-based vertical-port placement and terrain-adaptive UAV routing can substantially improve AED accessibility and response times in mountainous regions. The LAFEM model aligns with U-space airspace regulations and supports safe, automated AED deployment for improved outcomes in OHCA emergencies. Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones: 2nd Edition)
►▼ Show Figures

Figure 1

Review

Jump to: Research

33 pages, 4743 KB  
Review
Advances in Trajectory Prediction for High-Speed UAVs: A Review
by Wenqin Han, Shuangxi Liu, Xianyu Wu and Wei Zhao
Drones 2026, 10(7), 553; https://doi.org/10.3390/drones10070553 - 21 Jul 2026
Viewed by 399
Abstract
High-speed Unmanned Aerial Vehicles (UAVs), characterized by high velocity and maneuverability, represent critical strategic threats within the aerospace security domain. Accurate trajectory prediction is a fundamental prerequisite for effective early warning and decision-making in defense. Despite increasing research in the domain, model-, data-, [...] Read more.
High-speed Unmanned Aerial Vehicles (UAVs), characterized by high velocity and maneuverability, represent critical strategic threats within the aerospace security domain. Accurate trajectory prediction is a fundamental prerequisite for effective early warning and decision-making in defense. Despite increasing research in the domain, model-, data-, and hybrid-driven methods have not yet been comprehensively examined under a unified framework, limiting the understanding of their relative strengths and applicability. To address this gap, this paper systematically reviews the evolution and current technical status of these paradigms, categorized by the core logic underlying prediction methods. The principles and applicability limits of physical process modeling and state estimation algorithms are analyzed, along with emerging applications of machine learning and deep learning for trajectory feature extraction and pattern recognition. State-of-the-art architectures involving the integration of physical constraints and data-driven learning are discussed. Standard evaluation metrics are introduced to facilitate performance benchmarking of existing methods. Comparative analysis reveals that no single technical route can fully address the coupled challenges of uncertainty, accuracy, and real-time performance, underscoring that hybrid frameworks are essential for balancing these competing requirements. Lastly, key challenges are summarized, and future research directions are outlined to advance trajectory prediction methodologies. The provided insights can inform method selection and promote the development of high-accuracy prediction systems. Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones: 2nd Edition)
►▼ Show Figures

Figure 1

48 pages, 10103 KB  
Review
A Survey of Risk-Calibrated Certifiably Safe and Resource-Aware (RCSR) Path Planning for Unmanned Aerial Vehicles
by Nathan Johnson, Sima Shafaei, Andrew Karem and Sayani Sarkar
Drones 2026, 10(5), 351; https://doi.org/10.3390/drones10050351 - 7 May 2026
Viewed by 1228
Abstract
Effective mission planning, path search, and path following are critical for unmanned aerial vehicles (UAVs) operating in complex, dynamic, and resource-constrained environments. Classical path planning approaches, including graph-based search, sampling-based methods, and trajectory optimization, provide structured solutions with performance guarantees but often exhibit [...] Read more.
Effective mission planning, path search, and path following are critical for unmanned aerial vehicles (UAVs) operating in complex, dynamic, and resource-constrained environments. Classical path planning approaches, including graph-based search, sampling-based methods, and trajectory optimization, provide structured solutions with performance guarantees but often exhibit limited adaptability to uncertainty, environmental disturbances, and evolving mission constraints. Reinforcement learning (RL) offers a complementary capability by enabling adaptive decision-making and online response to dynamic obstacles and partial observability. This paper examines UAV path planning and navigation within a Risk-Calibrated, Certifiably Safe, and Resource-Aware (RCSR) framework, with emphasis on its implications for mission planning, path search, and path following. Classical planning techniques are reviewed alongside recent advances in RL-based navigation for single-UAV and multi-UAV systems. Particular attention is given to safe reinforcement learning, constrained optimization, and runtime assurance mechanisms that address safety, regulatory compliance, and resource limitations in real-world deployments. Through a comparative analysis of classical, learning-based, and hybrid planning architectures, this work highlights key trade-offs among adaptability, safety, computational cost, and energy efficiency. The paper concludes by identifying hybrid learning–planning approaches as a practical direction for scalable, reliable, and deployable UAV mission planning systems. Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones: 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop