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Applied Sciences
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Next-Generation Mobile Robotics: Intelligent Navigation, Adaptive Planning, and Sensor Integration
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Next-Generation Mobile Robotics: Intelligent Navigation, Adaptive Planning, and Sensor Integration

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 1727

Special Issue Editors

Prof. Dr. Edgar Martínez-García

E-Mail Website
Guest Editor
Laboratorio de Robótica, Institute of Engineering and Technology, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Interests: autonomous navigation; path planning; mobile robotics; robust perception; multi-sensor integration; legged locomotion; adaptive locomotion; control engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Rajesh Elara Mohan

E-Mail Website
Guest Editor
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
Interests: reconfigurable robotics; robot ergonomics; bio-inspired design; robotics foresight; human-robot interaction; assistive technologies

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the latest advancements and emerging trends in mobile robotics, with a focus on intelligent navigation, adaptive path planning, and sensor integration. We welcome research that tackles challenges in navigating dynamic and unstructured environments, advancements in simultaneous localization and mapping, and coordination and navigation within multi-robot systems in all modalities, such as underwater, aerial and terrestrial robots. Contributions exploring AI-driven vehicles and context-aware path planning, including resilient planning under uncertainty and real-time adaptive strategies for dynamic obstacles, are of interest. Further, we encourage exploration of energy-efficient locomotion, adaptive gait generation, and stability control for multi-legged robots, alongside biologically inspired cognitive navigation and decentralized planning for swarm intelligence. Research investigating morphology-aware navigation for shape-shifting and modular robots, collaborative navigation in reconfigurable systems, and bio-inspired strategies for seamless transitions between locomotion modes are also highly relevant.

Additionally, this Special Issue will highlight advancements in real-time reconfiguration for obstacle avoidance and autonomous adaptation to environmental demands, AI-enhanced methods for real-time perception and proprioceptive sensing for adaptive terrain response, and learning-based terrain classification and adaptive locomotion. We seek contributions that demonstrate the effective integration of vision and other sensors for robust obstacle detection and response, as well as those that explore the synergistic combination of environmental sensing and morphology control to enhance robotic adaptability across diverse operational scenarios.

This Special Issue also explores the application of autonomous robots in critical domains such as self-driving vehicles, logistics, delivery, disaster response, search-and-rescue operations, smart agriculture, precision farming, healthcare, and assistive technologies. Additionally, it highlights emerging topics like quantum-inspired algorithms for navigation and planning, as well as long-term autonomy with self-learning capabilities.

We invite researchers to submit their work to advance these transformative areas in robotics. Contributions should push the boundaries of mobile robotics and drive the future of autonomous systems.

Dr. Edgar Martínez-García
Dr. Rajesh Elara Mohan
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. Applied Sciences 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

  • autonomous navigation
  • path planning
  • mobile robotics
  • robust perception
  • multi-sensor integration
  • SLAM
  • legged locomotion
  • gait optimization
  • adaptive locomotion
  • swarm intelligence
  • multi-robot systems

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Published Papers (2 papers)

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Research

19 pages, 3075 KB  
Article
Implementation of Robotic Surface-to-Surface Object Transfer on a Quadrupedal Platform
by Woosung Lim and Jungwon Seo
Appl. Sci. 2026, 16(5), 2590; https://doi.org/10.3390/app16052590 - 8 Mar 2026
Viewed by 329
Abstract
This paper investigates robotic surface-to-surface object transfer, a release manipulation task in which a robot transfers an object from an end-effector that functions solely as a large supporting surface to an external surface such as the ground. Such transfers commonly arise in many [...] Read more.
This paper investigates robotic surface-to-surface object transfer, a release manipulation task in which a robot transfers an object from an end-effector that functions solely as a large supporting surface to an external surface such as the ground. Such transfers commonly arise in many practical manipulation scenarios. Unlike simple releasing actions, surface-to-surface transfer requires maintaining force equilibrium through controlled rolling and sliding at the contact interfaces. We present a manipulation model that captures the essential contact kinematics and enables force balance throughout the transfer. To assess robustness, we introduce a stability simulation framework that evaluates dynamic stability by monitoring variations in gravitational potential energy across object configurations. The proposed approach is implemented on a quadrupedal robot and validated through a series of experiments with objects of varying geometries. The results demonstrate the effectiveness of the method and underscore the role of stability-aware control in surface-to-surface transfer. Full article
(This article belongs to the Special Issue Next-Generation Mobile Robotics: Intelligent Navigation, Adaptive Planning, and Sensor Integration)
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23 pages, 3037 KB  
Article
Depth Matters: Geometry-Aware RGB-D-Based Transformer-Enabled Deep Reinforcement Learning for Mapless Navigation
by Alpaslan Burak İnner and Mohammed E. Chachoua
Appl. Sci. 2026, 16(3), 1242; https://doi.org/10.3390/app16031242 - 26 Jan 2026
Cited by 1 | Viewed by 715
Abstract
Autonomous navigation in unknown environments demands policies that can jointly perceive semantic context and geometric safety. Existing Transformer-enabled deep reinforcement learning (DRL) frameworks, such as the Goal-guided Transformer Soft Actor–Critic (GoT-SAC), rely on temporal stacking of multiple RGB frames, which encodes short-term motion [...] Read more.
Autonomous navigation in unknown environments demands policies that can jointly perceive semantic context and geometric safety. Existing Transformer-enabled deep reinforcement learning (DRL) frameworks, such as the Goal-guided Transformer Soft Actor–Critic (GoT-SAC), rely on temporal stacking of multiple RGB frames, which encodes short-term motion cues but lacks explicit spatial understanding. This study introduces a geometry-aware RGB-D early fusion modality that replaces temporal redundancy with cross-modal alignment between appearance and depth. Within the GoT-SAC framework, we integrate a pixel-aligned RGB-D input into the Transformer encoder, enabling the attention mechanism to simultaneously capture semantic textures and obstacle geometry. A comprehensive systematic ablation study was conducted across five modality variants (4RGB, RGB-D, G-D, 4G-D, and 4RGB-D) and three fusion strategies (early, parallel, and late) under identical hyperparameter settings in a controlled simulation environment. The proposed RGB-D early fusion achieved a 40.0% success rate and +94.1 average reward, surpassing the canonical 4RGB baseline (28.0% success, +35.2 reward), while a tuned configuration further improved performance to 54.0% success and +146.8 reward. These results establish early pixel-level multimodal fusion (RGB-D) as a principled and efficient successor to temporal stacking, yielding higher stability, sample efficiency, and geometry-aware decision-making. This work provides the first controlled evidence that spatially aligned multimodal fusion within Transformer-based DRL significantly enhances mapless navigation performance and offers a reproducible foundation for sim-to-real transfer in autonomous mobile robots. Full article
(This article belongs to the Special Issue Next-Generation Mobile Robotics: Intelligent Navigation, Adaptive Planning, and Sensor Integration)
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