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Electronics
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Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots
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Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 3877

Editors

Prof. Dr. Ulises Orozco-Rosas

E-Mail Website
Guest Editor
School of Engineering, CETYS Universidad, Tijuana 22210, Mexico
Interests: artificial intelligence; quantum computing; machine learning; autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Oscar Montiel Ross

E-Mail Website
Guest Editor
Centro de Investigación y Desarrollo de Tecnología Digital, Instituto Politécnico Nacional, Mexico City 07738, Mexico
Interests: intelligent systems; quantum computing; quantum intelligent systems; evolutionary computation; fuzzy systems; neural networks; deep learning; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of autonomous vehicles and intelligent robots has significantly impacted transportation, exploration, manufacturing, agriculture, and other sectors. These systems now play a crucial role in the growth of the self-driving car industry, logistics, aerospace, disaster relief, scientific exploration, and security, among other fields.

Path planning, perception, and control are essential components of autonomous navigation. Advances in these areas are key to achieving stability, scalability, flexibility, safety, robustness, and efficiency in autonomous vehicles and mobile robots—objectives that remain challenging in complex and dynamic environments.

This Special Issue, titled “Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots”, will showcase the latest trends in path planning, autonomous navigation, mobile robotics, and artificial intelligence while addressing current advancements and persistent challenges in the field. We are seeking innovative research contributions on topics such as path planning, perception, control, and autonomous navigation.

We welcome submissions presenting novel algorithms, architectures, and systems that enable robust, safe, and efficient autonomous navigation. The scope includes theoretical developments, innovative algorithmic approaches, experimental validations, and real-world deployments. Special emphasis will be placed on interdisciplinary solutions that integrate perception, reasoning, and control to address the complexities of real-world scenarios involving both autonomous vehicles and intelligent robots.

We welcome reviews and original research articles focused on but not limited to the following topics:

  • Path planning and trajectory generation;
  • Robust techniques of motion control and motion planning;
  • Perception, reasoning, communication, adaptation, and learning;
  • Self-localization, mapping, navigation, and simultaneous localization and mapping;
  • Autonomous navigation in real environments and complex scenarios;
  • Autonomy, intelligent behaviors, and evolutionary and bio-inspired robots;
  • Deep learning and reinforcement learning for autonomous vehicles;
  • Multirobot and multi-agent systems, cooperation, and collaboration;
  • Optimization and optimal control for autonomous vehicles;
  • Industrial and agricultural applications of intelligent robots.

Prof. Dr. Ulises Orozco-Rosas
Prof. Dr. Oscar Montiel Ross
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. Electronics 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 2400 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
  • autonomous navigation systems
  • intelligent mobile robots
  • motion control and trajectory generation
  • sensor fusion
  • explainable artificial intelligence (XAI)
  • simultaneous localization and mapping (SLAM)
  • multi-robot coordination
  • collision avoidance
  • optimization and optimal control

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

Published Papers (5 papers)

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Research

Jump to: Review

20 pages, 3283 KB  
Article
Ring-Shaped Wheeled Mobile Robot Circulation with Modified Van der Pol Limit-Cycle Reference
by Jesus Quiros, Luis T. Aguilar, Ulises Orozco-Rosas and Victor Manuel Juárez-Luna
Electronics 2026, 15(11), 2458; https://doi.org/10.3390/electronics15112458 - 4 Jun 2026
Viewed by 277
Abstract
Defining and tracking trajectories in complex environments for nonholonomic mobile robots are challenging due to the underactuated dynamics and nonintegrable velocity constraints of these robots, which preclude smooth, time-invariant feedback stabilization and yield uncontrollable linearizations around equilibrium points. As a result, maintaining structured [...] Read more.
Defining and tracking trajectories in complex environments for nonholonomic mobile robots are challenging due to the underactuated dynamics and nonintegrable velocity constraints of these robots, which preclude smooth, time-invariant feedback stabilization and yield uncontrollable linearizations around equilibrium points. As a result, maintaining structured motions such as ring-shaped limit cycles becomes particularly difficult under large initial deviations or external disturbances. In this paper, a control framework based on a dynamically generated reference trajectory is proposed, where the desired motion is defined by a modified Van der Pol oscillator. Unlike conventional approaches relying on predefined geometric paths, the proposed method embeds the target orbit into a dynamic auxiliary nonlinear system whose trajectories converge to a stable limit cycle, enabling local asymptotic convergence to the desired motion. A discontinuous robust control law is designed for a perturbed wheeled mobile robot, and the resulting closed-loop system is analyzed within the framework of solutions of systems with discontinuous right-hand sides. It is shown that the tracking error dynamics are uniformly and ultimately bounded with respect to matched disturbances and that, in the disturbance-free case, the tracking errors converge asymptotically to the origin. As a consequence, the robot’s trajectory converges to the invariant limit cycle of the reference dynamics, therebydriving the robot’s trajectory toward the invariant limit cycle of the reference dynamics. The simulation results demonstrate an improvement in the transient response relative to standard circular reference tracking. The experimental results further corroborate these findings, showing that the modified Van der Pol reference keeps the position tracking errors tightly bounded, while mitigating the large initial overshoot associated with the circular reference. Full article
(This article belongs to the Special Issue Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots)
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27 pages, 4008 KB  
Article
Cross-Dataset Insights for Fine-Grained Vehicle Orientation Prediction
by Tomas Pasaulis, Robertas Pečeliūnas, Vidas Žuraulis, Vidas Raudonis, Tomyslav Sledevič and Dalius Matuzevičius
Electronics 2026, 15(10), 2097; https://doi.org/10.3390/electronics15102097 - 14 May 2026
Viewed by 440
Abstract
Fine-grained vehicle orientation estimation is widely reported with strong in-domain accuracy, yet performance degrades substantially when models are applied across datasets; the relative contributions of visual domain shift and annotation label incompatibility to this degradation remain poorly understood. A controlled cross-dataset benchmark was [...] Read more.
Fine-grained vehicle orientation estimation is widely reported with strong in-domain accuracy, yet performance degrades substantially when models are applied across datasets; the relative contributions of visual domain shift and annotation label incompatibility to this degradation remain poorly understood. A controlled cross-dataset benchmark was conducted using two publicly available datasets—Car Full View (CFV) and Freiburg Static Cars 52 v1.1 (UnsupCar)—under a fixed ConvNeXt-Small predictor with a varied training source, test target, and image preprocessing strategy. All conditions were evaluated with five-fold cross-validation at the vehicle-instance level. Annotation label incompatibility was identified as the dominant source of transfer error: correcting the angular convention mismatch in UnsupCar orientation labels reduced cross-dataset circular mean absolute error (CMAE) by approximately 3.5–4.5∘. Crop protocol was a similarly large factor—train/test crop mismatch raised CMAE into the 9–12∘ range. Square cropping with mirrored boundary padding provided the most robust preprocessing across both in-domain and cross-dataset conditions. After label harmonization, a residual transfer gap of approximately 2∘ remained, with a consistent directional asymmetry favoring the UnsupCar-to-CFV transfer direction. Joint training on both harmonized datasets achieved the best-balanced performance (3.77∘ on CFV; 5.38∘ on UnsupCar). These results demonstrate that instance-level splitting, explicit label harmonization, and consistent crop definition are necessary preconditions for credible cross-dataset vehicle orientation evaluation. Full article
(This article belongs to the Special Issue Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots)
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29 pages, 31856 KB  
Article
A Vision–Locomotion Framework Toward Obstacle Avoidance for a Bio-Inspired Gecko Robot
by Wenrui Xiang, Barmak Honarvar Shakibaei Asli and Aihong Ji
Electronics 2026, 15(4), 882; https://doi.org/10.3390/electronics15040882 - 20 Feb 2026
Cited by 1 | Viewed by 706
Abstract
This paper presents the design and experimental evaluation of a bio-inspired gecko robot, focusing on mechanical design, vision-based obstacle perception, and rhythmic locomotion control as enabling technologies for future obstacle avoidance in complex environments. The robot features a 17-degrees-of-freedom mechanical structure with a [...] Read more.
This paper presents the design and experimental evaluation of a bio-inspired gecko robot, focusing on mechanical design, vision-based obstacle perception, and rhythmic locomotion control as enabling technologies for future obstacle avoidance in complex environments. The robot features a 17-degrees-of-freedom mechanical structure with a flexible spine and multi-jointed limbs, providing a physical basis for adaptive locomotion. For perception, a custom obstacle detection dataset was constructed from the robot’s onboard camera view and used to train a YOLOv5-based detection model. Experimental results show that the trained model achieves a mean average precision (mAP) of 0.979 and a maximum F1-score of 0.97 at an optimal confidence threshold, demonstrating reliable real-time obstacle perception under diverse indoor conditions. For motion control, a central pattern generator (CPG) based on Hopf oscillators is implemented to generate rhythmic locomotion. Experimental evaluations confirm stable diagonal gait generation, with coordinated joint trajectories oscillating at 1 Hz. The flexible spine exhibits periodic lateral deflection with peak amplitudes of ±15°, ±10°, and ±8° across spinal joints, enhancing locomotion continuity and turning capability. Physical robot experiments further demonstrate smooth straight-line crawling enabled by the coupled limb–spine motion. While visual perception and CPG-based locomotion are experimentally validated as independent subsystems, their real-time closed-loop integration is not implemented in this study. Instead, this work establishes a system-level framework and experimental baseline for future perception–motion coupling, providing a foundation for closed-loop obstacle avoidance and autonomous navigation in bio-inspired gecko robots. Full article
(This article belongs to the Special Issue Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots)
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28 pages, 5972 KB  
Article
ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs
by Micaela Jara Ten Kathen, Natalia Benitez, Mario Arzamendia and Daniel Gutiérrez Reina
Electronics 2026, 15(3), 676; https://doi.org/10.3390/electronics15030676 - 4 Feb 2026
Cited by 1 | Viewed by 679
Abstract
Autonomous surface vehicles can support water-quality monitoring, but they require planners that place measurements where they most improve the environmental estimate under mission constraints. This paper proposes ACO-Path, an informative path planner that couples Ant Colony Optimization -Ant System- with online Gaussian Process [...] Read more.
Autonomous surface vehicles can support water-quality monitoring, but they require planners that place measurements where they most improve the environmental estimate under mission constraints. This paper proposes ACO-Path, an informative path planner that couples Ant Colony Optimization -Ant System- with online Gaussian Process mapping. During the mission, the Gaussian Process updates a mean or contamination map and a variance or uncertainty map, from which dynamic action zones are derived and used to guide an explicit explore then exploit policy. The method is evaluated in a simulated water resource monitoring scenario inspired by Lake Ypacaraí, considering three exploration distances and two heuristic weights. In a comparison against five baseline planners, ACO-Path achieves the lowest hotspot error, Errorpeak=0.19896±0.39400, while remaining competitive in global reconstruction, MSEmap=0.00144±0.00348, R2=0.96066±0.09861. In addition, a turning analysis based on the absolute heading change between consecutive segments |Δα| shows that ACO-Path produces smoother trajectories, with fewer sharp turns |Δα|≥45° than counterpart baselines under the same mission constraints. Full article
(This article belongs to the Special Issue Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots)
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Review

Jump to: Research

41 pages, 12659 KB  
Review
A Survey of Machine Learning Algorithms for Autonomous Vehicles
by Agnieszka Lazarowska, Monika Rybczak, Mirosław Łącki, Krystian Kozakiewicz, Józef Lisowski and Andrzej Stateczny
Electronics 2026, 15(10), 2073; https://doi.org/10.3390/electronics15102073 - 13 May 2026
Viewed by 964
Abstract
This paper presents a comprehensive review of recent works (2020–2026) on machine learning (ML) algorithms applied to autonomous platforms such as unmanned underwater vehicles (UUVs), unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and ground-based mobile robots. The review focuses on the following [...] Read more.
This paper presents a comprehensive review of recent works (2020–2026) on machine learning (ML) algorithms applied to autonomous platforms such as unmanned underwater vehicles (UUVs), unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and ground-based mobile robots. The review focuses on the following functional areas: environment perception, simultaneous localization and mapping (SLAM), collision avoidance and path planning, and motion control. Different ML methods are covered, including supervised, semi-supervised, and unsupervised learning, as well as reinforcement learning and deep reinforcement learning. The reviewed methods are analyzed with respect to their performance, robustness, and suitability for different operational environments, including underwater, surface, air, and land domains. Finally, the authors identify key challenges and outline promising future directions aimed at improving the safety, autonomy, and reliability of autonomous vehicles. Full article
(This article belongs to the Special Issue Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots)
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