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Advancements in Autonomous Navigation Systems for UAVs
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Advancements in Autonomous Navigation Systems for UAVs

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1254

Special Issue Editors

Prof. Dr. Vladan Papic

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia
Interests: computer vision; expert systems; robotics; motion analysis; UAV
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Josip Music

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia
Interests: mobile robotics; AI in robotics; data fusion; human–machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advancements in satellite technology and AI, the integration of GPS, inertial measurement units (IMUs), and computer vision has opened up new possibilities in the development of UAV navigation systems. This progress in navigation systems is changing our understanding of drone operations.

Autonomous drone navigation systems use increasingly sophisticated algorithms and sensors to navigate complex environments. These systems not only improve flight safety and efficiency but also lead to new and exciting applications in a variety of fields.

This Special Issue will be dedicated to the latest advances and findings in hardware and software solutions, including algorithms, for autonomous navigation systems in UAVs. In particular, attention is given to various UAV sensors, onboard computers, path planning, AI, ML, and SLAM

We welcome papers presenting multidisciplinary and state-of-the-art research on navigation systems in various types of applications across diverse fields, including precision agriculture, surveillance, infrastructure inspection, search and rescue, and environmental monitoring.

Prof. Dr. Vladan Papic
Prof. Dr. Josip Music
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. Sensors is an international peer-reviewed open access semimonthly 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

  • autonomous navigation systems
  • UAV sensors
  • path planning
  • SLAM
  • onboard computer
  • autonomous drones
  • artificial intelligence

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Published Papers (1 paper)

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Research

19 pages, 6107 KB  
Article
Robust Path Planning via Deep Reinforcement Learning
by Daeyeol Kang, Jongyoon Park and Pileun Kim
Sensors 2026, 26(9), 2658; https://doi.org/10.3390/s26092658 - 24 Apr 2026
Viewed by 768
Abstract
Deep reinforcement learning (DRL) for autonomous mobile robot navigation faces several inherent limitations. The stochastic nature of actions generated by DRL policies can undermine performance consistency, while inefficient exploration frequently delays the learning process or prevents the discovery of optimal solutions. This research [...] Read more.
Deep reinforcement learning (DRL) for autonomous mobile robot navigation faces several inherent limitations. The stochastic nature of actions generated by DRL policies can undermine performance consistency, while inefficient exploration frequently delays the learning process or prevents the discovery of optimal solutions. This research aims to enhance the robustness of path planning by addressing these challenges. To achieve this goal, we propose a hybrid approach that integrates the flexible decision-making capabilities of deep reinforcement learning with the stability of traditional path planning. The proposed model adopts the Twin Delayed Deep Deterministic Policy Gradient (TD3) network as its base. Notably, we pre-process LiDAR point cloud data to extract only essential features for the state representation, thereby preventing performance degradation from high-dimensional inputs and improving computational efficiency. Our model optimizes the learning process through two core strategies. First, it prioritizes experience data generated during training based on negative rewards, guiding the model to learn more frequently from critical failures rather than redundant successes. Second, it dynamically compares the action proposed by the TD3 network with a goal-oriented action from a classical path-planning algorithm in real time. By selecting the action with the higher estimated value, the model guides the policy toward a stable and effective trajectory from the earliest stages of training. To validate the efficacy of our approach, we conducted simulation-based experiments comparing the performance of the proposed model with existing reinforcement learning networks. To ensure statistical significance and mitigate the impact of random initialization, all reported results are averaged over 10 independent runs with different random seeds. The results quantitatively demonstrate that our model achieves significantly higher and more stable reward values, confirming a robust improvement in the path-planning process. Full article
(This article belongs to the Special Issue Advancements in Autonomous Navigation Systems for UAVs)
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