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Path Planning and Navigation for Autonomous Vehicles and Intelligent Robots

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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 1208

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Special Issue Editors

Prof. Dr. Ulises Orozco-Rosas

E-Mail Website
Guest Editor

School of Engineering, CETYS Universidad, Tijuana 22210, BC, Mexico
Interests: deep learning; computational intelligence; artificial intelligence; evolutionary computation; parallel GPU computing; intelligent systems; autonomous vehicles; mobile robots

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
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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-blind 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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Published Papers (2 papers)

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Research

29 pages, 31856 KB

Open AccessFeature PaperArticle

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

Viewed by 333

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 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

Open AccessArticle

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

Viewed by 371

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 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,

E r r o r_{peak} = 0.19896 \pm 0.39400

, while remaining competitive in global reconstruction,

M S E_{map} = 0.00144 \pm 0.00348

R^{2} = 0.96066 \pm 0.09861

. In addition, a turning analysis based on the absolute heading change between consecutive segments

| Δ α |

shows that ACO-Path produces smoother trajectories, with fewer sharp turns

| Δ α | \geq 45 °

than counterpart baselines under the same mission constraints. Full article