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Drones
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UAV Path Planning Algorithms for Surveillance and Reconnaissance in Civil...
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UAV Path Planning Algorithms for Surveillance and Reconnaissance in Civil Applications

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

Deadline for manuscript submissions: 28 August 2026 | Viewed by 1407

Special Issue Editors

Dr. Octavio Garcia-Salazar

E-Mail Website
Guest Editor
Aerospace Engineering Research and Innovation Center, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo Leon, Apodaca 66616, Nuevo Leon, Mexico
Interests: UAS; UAVs; drones; flight dynamics; control theory; robotics; path planning; trajectory track
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anand Sanchez-Orta

E-Mail Website
Guest Editor
Robotics and Advanced Manufacturing Division, CINVESTAV, Saltillo, Coahuila, Mexico
Interests: UAVs; control; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Aldo Jonathan Muñoz-Vazquez

E-Mail Website
Guest Editor
Department of Computer, Electrical & Software Engineering, Embry-Riddle Aeronautical University, Prescott, AZ 86301-3720, USA
Interests: fractional calculus; nonlinear systems; robotics; fuzzy logics; neural networks; control theory; integral equations; robotics and autonomous systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advances in UAV path planning algorithms designed for surveillance and reconnaissance in civil applications. As unmanned aerial systems become increasingly integral to tasks such as environmental monitoring, infrastructure inspection, disaster response, and public safety, the need for robust, intelligent, and adaptive navigation strategies has grown significantly. This Issue aims to showcase innovative approaches in autonomous navigation, including the integration of machine learning techniques for real-time decision-making, obstacle avoidance, cooperative multi-UAV coordination, and trajectory optimization in complex and dynamic environments. Manuscripts related but not limited to design and development, guidance, navigation, and control (GNC), virtual simulations, and ground testing of UAVs are welcome. We invite researchers and engineers to contribute their original research, focusing on (but not limited to) the topics listed below:

  • Path planning algorithms
  • Civil surveillance
  • Reconnaissance missions
  • Autonomous navigation
  • Obstacle avoidance
  • Multi-UAV systems
  • Machine learning for navigation
  • Deep reinforcement learning
  • Real-time trajectory optimization
  • Cooperative motion planning
  • Sensor fusion
  • Urban monitoring
  • Disaster response
  • Infrastructure inspection
  • Rotary-wing, fixed-wing and convertible UAVs
  • Ethics and regulation in UAV operations

Prof. Dr. Octavio Garcia-Salazar
Prof. Dr. Anand Sanchez-Orta
Dr. Aldo Jonathan Muñoz-Vazquez
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-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

  • path planning algorithms
  • civil surveillance
  • reconnaissance missions
  • autonomous navigation
  • obstacle avoidance
  • multi-UAV systems
  • machine learning for navigation
  • deep reinforcement learning
  • real-time trajectory optimization
  • cooperative motion planning
  • sensor fusion
  • urban monitoring
  • disaster response
  • infrastructure inspection
  • rotary-wing, fixed-wing and convertible UAVs
  • ethics and regulation in UAV operations

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

Published Papers (2 papers)

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Research

26 pages, 2977 KB  
Article
HGR-QL: Optimized Q-Learning for Multi-UAV Path Planning in Mountain Search and Rescue
by Qi Liu, Daqiao Zhang, Shaopeng Li, Pei Dai and Wenjing Li
Drones 2026, 10(3), 223; https://doi.org/10.3390/drones10030223 - 22 Mar 2026
Viewed by 370
Abstract
Existing Q-Learning-based path planning methods face significant bottlenecks in large-scale collaboration, dynamic interference adaptation, and regional value differentiation, failing to meet the practical needs of mountain search and rescue. This study proposes HGR-QL, an optimized Q-Learning method for large-scale multi-UAV operations. Referencing remote [...] Read more.
Existing Q-Learning-based path planning methods face significant bottlenecks in large-scale collaboration, dynamic interference adaptation, and regional value differentiation, failing to meet the practical needs of mountain search and rescue. This study proposes HGR-QL, an optimized Q-Learning method for large-scale multi-UAV operations. Referencing remote sensing datasets, a 50 × 50 dynamic grid environment is constructed by integrating 20% fixed obstacles and 10 moving interference sources, highly simulating real mountain features. Integrating the individual Q-tables and the regional shared Q-tables, the hierarchical independent Q-table architecture is designed, balancing local autonomy and global collaboration. To guide UAVs focusing on remote sensing-identified high-value areas, an innovative multi-level gradient collision avoidance reward function is constructed, avoiding task deviation. Comparative experiments across three scenarios with four baselines and ablation tests validate the core modules. Results show HGR-QL outperforms peers in key metrics: in the dynamic interference scenario, it achieves a 74.47% task completion rate, 25.44 collisions, and a stable 100.00 ms communication delay. HGR-QL provides a lightweight, scalable solution, effectively enhancing the efficiency, safety, and stability of mountain search and rescue and supporting the “golden 72 h” rescue window. Full article
(This article belongs to the Special Issue UAV Path Planning Algorithms for Surveillance and Reconnaissance in Civil Applications)
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31 pages, 9741 KB  
Article
RG-HDP-VD: A Physics-Aware Cooperative Trajectory Planning Framework for Heterogeneous Multi-UAVs
by Dan Han, Zhaoyuan Hua, Xinyu Zhu, Liang Luo, Hao Jiang and Lifang Wang
Drones 2026, 10(3), 192; https://doi.org/10.3390/drones10030192 - 10 Mar 2026
Viewed by 329
Abstract
This paper presents Regret-Guided Heuristic Decentralized Prioritized Planning with Velocity Decomposition (RG-HDP-VD), a physics-aware cooperative trajectory planning framework for heterogeneous Unmanned Aerial Vehicles (UAVs) relief delivery in post-earthquake, non-convex canyon environments. RG-HDP-VD addresses two prevalent failure modes: energy-inefficient congestion caused by ignoring time-varying [...] Read more.
This paper presents Regret-Guided Heuristic Decentralized Prioritized Planning with Velocity Decomposition (RG-HDP-VD), a physics-aware cooperative trajectory planning framework for heterogeneous Unmanned Aerial Vehicles (UAVs) relief delivery in post-earthquake, non-convex canyon environments. RG-HDP-VD addresses two prevalent failure modes: energy-inefficient congestion caused by ignoring time-varying payload dynamics, and the collapse of feasible sets due to strict arrival windows in fixed-speed planning. We construct a mass-augmented energy topology and use a mass-augmented energy-aware A* search to extract baseline physical metrics—path length, total energy, and unit-distance energy—for each UAV. Regret-Guided (RG) arbitration then quantifies the relative energy cost of waiting versus detouring at conflicts and grants right-of-way to heavy-load, high-cost platforms. These priorities are embedded into Heuristic Decentralized Prioritized Planning (HDP), which maintains a global spatiotemporal occupancy map and serializes planning to eliminate deadlocks. To satisfy tight time windows, Velocity Decomposition (VD) maps 4D temporal constraints into a 3D path-length feasible interval and is realized via an improved VD-TSRRT* sampling-based planner. In high-fidelity simulations, RG-HDP-VD demonstrates superior scalability over conventional methods, maintaining high success rates (up to 100%) in saturated scenarios, while reducing average planning time by ~45% and total system energy by 6.7%. Finally, real-world flight demonstrations using a heterogeneous quadrotor team validate the framework’s practical feasibility and robust hardware execution. Full article
(This article belongs to the Special Issue UAV Path Planning Algorithms for Surveillance and Reconnaissance in Civil Applications)
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