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Sensors
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Advanced Sensors for Path Planning and Navigation in Challenging Environments
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Advanced Sensors for Path Planning and Navigation in Challenging Environments

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1158

Special Issue Editors

Dr. Jianguo (Jack) Wang

E-Mail Website
Guest Editor
Transport Research Centre, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), 81 Broadway, Ultimo, NSW 2007, Australia
Interests: sensor fusion for surveying, navigation and perception; robotics and intelligent systems; environment-friendly transportation and housing; GNSS, IMU, vision and laser sensors modelling and data fusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gregor Klančar

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: autonomous mobile robots; motion control; trajectory tracking; path planning; localization; multiagent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Shiwei Lin

E-Mail Website
Guest Editor Assistant
College of Computer Engineering, Jimei University, Xiamen 361021, China
Interests: robot path planning and navigation; swarm intelligence algorithms; reinforcement learning; robot path planning; multi-robot scheduling and planning; multi-sensor fusion; indoor positioning

Special Issue Information

Dear Colleagues,

The proliferation of mobile robots, including Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs), demands more efficient and reliable operational solutions, which can be achieved by upgrading them to Autonomous Guided Vehicles (AGVs). Applying proper sensing technologies combined with navigation and path planning algorithms is a core factor that impacts the implementation and performance of AGVs for reliable and feasible autonomous guidance. There is an urgent need for the development of AGVs in challenging environments, such as urban and indoor areas, where various objects (e.g., buildings and moving subjects) cause multipath and signal delay problems for radio-based positioning (GNSS, etc.). Multiple alternative sensors should be used with proper fusion algorithms to achieve reliable and seamless navigation and positioning in immersive environments.

We encourage authors from academia and industry to publish new research results related to sensor fusion for AGV navigation and path planning in challenging environments.

Dr. Jianguo (Jack) Wang
Prof. Dr. Gregor Klančar
Guest Editors

Dr. Shiwei Lin
Guest Editor Assistant

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

  • positioning sensors
  • sensor fusion
  • navigation
  • AGV path planning
  • dynamic environment
  • AGV applications

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Published Papers (1 paper)

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Research

24 pages, 2181 KB  
Article
DPDQN-TER: An Improved Deep Reinforcement Learning Approach for Mobile Robot Path Planning in Dynamic Scenarios
by Shuyuan Gao, Yang Xu, Xiaoxiao Guo, Chenchen Liu and Xiaobai Wang
Sensors 2025, 25(21), 6741; https://doi.org/10.3390/s25216741 - 4 Nov 2025
Viewed by 958
Abstract
Efficient and stable path planning in dynamic and obstacle-dense environments, such as large-scale structure assembly measurement, is essential for improving the practicality and environmental adaptability of mobile robots in measurement and quality inspection tasks. However, traditional reinforcement learning methods often suffer from inefficient [...] Read more.
Efficient and stable path planning in dynamic and obstacle-dense environments, such as large-scale structure assembly measurement, is essential for improving the practicality and environmental adaptability of mobile robots in measurement and quality inspection tasks. However, traditional reinforcement learning methods often suffer from inefficient use of experience and limited capability to represent policy structures in complex dynamic scenarios. To overcome these limitations, this study proposes a method named DPDQN-TER that integrates Transformer-based sequence modeling with a multi-branch parameter policy network. The proposed method introduces a temporal-aware experience replay mechanism that employs multi-head self-attention to capture causal dependencies within state transition sequences. By dynamically weighting and sampling critical obstacle-avoidance experiences, this mechanism significantly improves learning efficiency and policy performance and stability in dynamic environments. Furthermore, a multi-branch parameter policy structure is designed to decouple continuous parameter generation tasks of different action categories into independent subnetworks, thereby reducing parameter interference and improving deployment-time efficiency. Extensive simulation experiments were conducted in both static and dynamic obstacle environments, as well as cross-environment validation. The results show that DPDQN-TER achieves higher success rates, shorter path lengths, and faster convergence compared with benchmark algorithms including Parameterized Deep Q-Network (PDQN), Multi-Pass Deep Q-Network (MPDQN), and PDQN-TER. Ablation studies further confirm that both the Transformer-enhanced replay mechanism and the multi-branch parameter policy network contribute significantly to these improvements. These findings demonstrate improved overall performance (e.g., success rate, path length, and convergence) and generalization capability of the proposed method, indicating its potential as a practical solution for autonomous navigation of mobile robots in complex industrial measurement scenarios. Full article
(This article belongs to the Special Issue Advanced Sensors for Path Planning and Navigation in Challenging Environments)
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