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Autonomous Navigation for Intelligent Robots: Perception, Planning, and Control
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Autonomous Navigation for Intelligent Robots: Perception, Planning, and Control

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

Deadline for manuscript submissions: 25 December 2026 | Viewed by 4118

Special Issue Editors

Prof. Dr. Weimin Zhang

E-Mail Website
Guest Editor
1. Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
2. Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: robot navigation positioning and servo operation
Dr. Xiaopeng Chen

E-Mail Website
Guest Editor
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: active visual SLAM; active exploration of unknown environments; multi-modal perception and interaction; mobile operation of legged robots
Dr. Fangxing Li

E-Mail Website
Guest Editor
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: ground mobile robots; ehabilitation robots

Special Issue Information

Dear Colleagues,

The rapid advancement of intelligent robots demands increasingly sophisticated autonomous navigation capabilities for complex and dynamic environments. This Special Issue aims to gather cutting-edge research that addresses the integrated challenges of perception, planning, and control, which are the core pillars of robotic autonomy. We invite contributions that explore novel algorithms and systems for robust environmental perception using multi-sensor fusion (e.g., LiDAR, cameras, and IMUs), semantic understanding, and simultaneous localization and mapping (SLAM). Furthermore, we seek innovative motion planning strategies for dynamic obstacle avoidance, long-range navigation, and multi-robot coordination. Finally, papers on advanced control theories for stable and agile robot motion, adaptive control under uncertainty, and learning-based control methodologies are highly welcome. By bridging these key areas, this Special Issue will serve as a platform for disseminating breakthroughs that enhance the intelligence, safety, and reliability of next-generation autonomous robots in applications ranging from industrial logistics and service robotics to autonomous vehicles and field exploration.

Prof. Dr. Weimin Zhang
Dr. Xiaopeng Chen
Dr. Fangxing Li
Guest Editors

Dr. Chandan Sheikder
Guest Editor Assistant

Manuscript Submission Information

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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
  • robot perception
  • motion planning
  • robot control
  • sensor fusion
  • SLAM (simultaneous localization and mapping)
  • path planning
  • intelligent robots
  • mobile robotics

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Published Papers (5 papers)

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Research

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21 pages, 2331 KB  
Article
ST-GICM: A Spatiotemporal Graph Learning Framework with Intrinsic Curiosity for Robust Autonomous Exploration
by Linqing He, Weifeng Liu and Wanyu Li
Sensors 2026, 26(11), 3307; https://doi.org/10.3390/s26113307 - 22 May 2026
Viewed by 222
Abstract
With recent advances in deep reinforcement learning (DRL) and graph neural networks (GNNs), graph-based autonomous exploration methods have significantly improved decision-making performance in complex environments. However, under partial observability and sparse-reward conditions, existing methods still struggle with long-horizon decision-making and sustained exploration. To [...] Read more.
With recent advances in deep reinforcement learning (DRL) and graph neural networks (GNNs), graph-based autonomous exploration methods have significantly improved decision-making performance in complex environments. However, under partial observability and sparse-reward conditions, existing methods still struggle with long-horizon decision-making and sustained exploration. To address these challenges, we propose a spatiotemporal graph learning framework, termed ST-GICM, that improves the robustness and efficiency of autonomous exploration by integrating graph-structured encoding, temporal memory, and an intrinsic curiosity mechanism. Specifically, a Graph Attention Network (GAT) and a Spatiotemporal Reasoning Core (STRC) are employed to dynamically encode the viewpoint graph and fuse temporal memory, thereby alleviating perceptual aliasing in graph-based exploration. In addition, an Intrinsic Prediction Module (IPM) is designed to generate intrinsic rewards based on the prediction error of graph-level latent representations, thereby encouraging sustained exploration. Experiments conducted in procedurally generated complex topological environments show that the proposed method outperforms existing baselines in terms of coverage rate, success rate, repeated revisit rate, and oscillation count, while maintaining trajectory costs comparable to those of the baselines. These results demonstrate the effectiveness and superiority of ST-GICM in partially observable environments under sparse-reward conditions. Full article
(This article belongs to the Special Issue Autonomous Navigation for Intelligent Robots: Perception, Planning, and Control)
25 pages, 1348 KB  
Article
An Adaptive Octile JPS and Fuzzy-DWA Fused Path Planning Algorithm for Indoor Home Environments
by Wei Li, Zhuoda Jia, Dawen Sun, Deng Han, Zhenyang Qin and Qianjin Liu
Sensors 2026, 26(11), 3300; https://doi.org/10.3390/s26113300 - 22 May 2026
Viewed by 215
Abstract
Home indoor environments are characterized by alternating open spaces and obstacle-cluttered regions, which pose critical challenges to the autonomous navigation of home service robots. Existing hybrid path planning algorithms generally suffer from three core limitations: low global search efficiency, weak global-local planning coordination, [...] Read more.
Home indoor environments are characterized by alternating open spaces and obstacle-cluttered regions, which pose critical challenges to the autonomous navigation of home service robots. Existing hybrid path planning algorithms generally suffer from three core limitations: low global search efficiency, weak global-local planning coordination, and poor dynamic scene adaptability. To tackle these issues, this paper presents a novel hierarchical path planning framework combining an enhanced Jump Point Search (JPS) and a fuzzy-optimized Dynamic Window Approach (DWA). In the global planning layer, an adaptive Octile heuristic JPS based on local obstacle density is designed to reduce redundant node expansion and accelerate global path search, with a bounded suboptimality guarantee. To bridge global and local planning, a look-ahead distance-based dynamic waypoint selection strategy is developed to match the optimal waypoint in real time according to the robot’s motion state and environmental complexity, enabling seamless coordination between global path guidance and local trajectory generation. In the local planning layer, a fuzzy logic controller is introduced to dynamically tune the weights of the DWA trajectory evaluation function, which significantly improves the robot’s dynamic obstacle avoidance capability and motion smoothness. Comparative simulation experiments verify that the proposed method not only outperforms the conventional hybrid path planning algorithm, reducing expanded nodes by 68.09% and global planning time by 52.94%, while improving dynamic obstacle avoidance success rate by 31.43% and overall navigation efficiency by 23.95%, it also achieves better comprehensive navigation performance than the widely adopted PSO-DWA comparison algorithm. The proposed framework shows superior comprehensive performance and is well suited for the indoor autonomous navigation of home service robots. Full article
(This article belongs to the Special Issue Autonomous Navigation for Intelligent Robots: Perception, Planning, and Control)
14 pages, 6383 KB  
Article
Reinforcement Learning-Based Control of a 4-Wheel Independent Steering Mobile Robot for Robust Path Tracking in Outdoor Environments
by Hyoseok Lee and Hyun-Min Joe
Sensors 2026, 26(6), 1761; https://doi.org/10.3390/s26061761 - 10 Mar 2026
Viewed by 603
Abstract
This paper proposes a reinforcement learning (RL)-based control method for robust path tracking of a 4-wheel independent steering (4WIS) mobile robot in outdoor rough terrain environments. Traditional wheeled robots typically suffer from limitations including mobility constraints in narrow spaces, path deviations caused by [...] Read more.
This paper proposes a reinforcement learning (RL)-based control method for robust path tracking of a 4-wheel independent steering (4WIS) mobile robot in outdoor rough terrain environments. Traditional wheeled robots typically suffer from limitations including mobility constraints in narrow spaces, path deviations caused by ground slip, and reduced traction on rough terrain. To address these challenges, we designed a 4WIS mobile robot and implemented an architecture that independently controls the steering and driving of each wheel. The RL state space is defined by look-ahead path information, robot pose, velocity, and tracking errors, while the action space consists of target angular velocity and steering angle. To ensure robust performance, we applied random path and terrain generation and implemented domain randomization for sensors and actuators based on empirical GPS and motor data. The proposed controller was validated against the Pure Pursuit algorithm through dynamic simulations and real-world experiments. In simulations mimicking outdoor terrain, the controller reduced lateral and heading RMSE by 6.32% and 16.00%, respectively. In actual outdoor environments, it reduced these errors by 21.54% and 4.78%, respectively. These results demonstrate that the proposed controller provides superior robust tracking performance in unstructured outdoor environments. Full article
(This article belongs to the Special Issue Autonomous Navigation for Intelligent Robots: Perception, Planning, and Control)
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15 pages, 960 KB  
Article
ArmTenna: Two-Armed RFID Explorer for Dynamic Warehouse Management
by Abdussalam A. Alajami and Rafael Pous
Sensors 2026, 26(5), 1513; https://doi.org/10.3390/s26051513 - 27 Feb 2026
Viewed by 450
Abstract
Efficient RFID spatial exploration in dynamic warehouse environments is challenging due to occlusions, sensing geometry constraints, and the weak coupling between information acquisition and navigation decisions. Many existing inventory robots treat RFID sensing as a passive data source during exploration, without explicitly optimizing [...] Read more.
Efficient RFID spatial exploration in dynamic warehouse environments is challenging due to occlusions, sensing geometry constraints, and the weak coupling between information acquisition and navigation decisions. Many existing inventory robots treat RFID sensing as a passive data source during exploration, without explicitly optimizing sensing pose or prioritizing inventory-driven frontiers, which can result in incomplete coverage and redundant traversal. This paper presents ArmTenna, an articulated mobile robotic platform that formulates RFID inventory exploration as an active perception problem. The system integrates dual 4-DOF robotic arms carrying directional UHF RFID antennas and a 2-DOF neck-mounted RGB-D camera, enabling adaptive interrogation of candidate regions. We propose a multi-modal frontier exploration framework that combines newly detected EPC tags, average RSSI values, and vision-based product detections into a composite utility function for goal selection. By embedding articulated antenna control directly into the frontier evaluation loop, the robot tightly couples sensing geometry with exploration decisions. Experimental validation with 150 tagged items across three separated warehouse zones shows that ArmTenna achieves up to 97% map coverage, compared to 72% for a baseline platform, while reducing missed-tag regions. These results demonstrate that integrating active sensing pose control with multi-modal frontier evaluation provides an effective and scalable solution for RFID-driven warehouse inventory automation. Full article
(This article belongs to the Special Issue Autonomous Navigation for Intelligent Robots: Perception, Planning, and Control)
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Review

Jump to: Research

51 pages, 2633 KB  
Review
Large-Scale Model-Enhanced Vision-Language Navigation: Recent Advances, Practical Applications, and Future Challenges
by Zecheng Li, Xiaolin Meng, Xu He, Youdong Zhang and Wenxuan Yin
Sensors 2026, 26(7), 2022; https://doi.org/10.3390/s26072022 - 24 Mar 2026
Viewed by 1925
Abstract
The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied cognition. Vision-Language Navigation (VLN) has evolved [...] Read more.
The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied cognition. Vision-Language Navigation (VLN) has evolved from geometry-driven to semantics-driven and, more recently, knowledge-driven approaches. With the introduction of Large Language Models (LLMs) and Vision-Language Models (VLMs), recent methods have achieved substantial improvements in instruction interpretation, cross-modal alignment, and reasoning-based planning. However, existing surveys primarily focus on traditional VLN settings and offer limited coverage of LLM-based VLN, particularly in relation to Sim2Real transfer and edge-oriented deployment. This paper presents a structured review of LLM-enabled VLN, covering four core components: instruction understanding, environment perception, high-level planning, and low-level control. Edge deployment and implementation requirements, datasets, and evaluation protocols are summarized, along with an analysis of task evolution from path-following to goal-oriented and demand-driven navigation. Key challenges, including reasoning complexity, spatial cognition, real-time efficiency, robustness, and Sim2Real adaptation, are examined. Future research directions, such as knowledge-enhanced navigation, multimodal integration, and world-model-based frameworks, are discussed. Overall, LLM-driven VLN is progressing toward deeper cognitive integration, supporting the development of more explainable, generalizable, and deployable embodied navigation systems. Full article
(This article belongs to the Special Issue Autonomous Navigation for Intelligent Robots: Perception, Planning, and Control)
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