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Vision-Guided System in Intelligent Autonomous Robots

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

Deadline for manuscript submissions: 15 March 2026 | Viewed by 410

Special Issue Editors


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Guest Editor
Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: photogrammetric computer vision; biomedical imaging; LiDAR; IMU; mobile robotics; simultaneous localization and mapping (SLAM); machine learning; sensor calibration; sensor fusion; numerical optimization
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Engineering & Energy College of Science, Technology, Engineering & Mathematics, Murdoch University, Murdoch, WA 6150, Australia
Interests: aerial and underwater robotics; linear and nonlinear controls; robust controls; unknown disturbance observers

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Guest Editor
Department of Robotics and Mechatronics, Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska St. 45C, 15-351 Bialystok, Poland
Interests: robotics; mobile robotics; robot cooperation; autonomous agents; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous intelligent robots and systems are at the forefront of innovation in robotics, automation, AI, and human-machine interaction. However, several key challenges remain unresolved in current research and development efforts. These challenges include the following: (1) AI methods are not yet capable of effectively addressing the combined challenges of visual perception, control, navigation, and deep learning. (2) Existing vision-based perception techniques require improvements in feature extraction and learning efficiency, which could be addressed through high-performance computing and have latency. 3) As visual perception networks become increasingly complex, their training costs rise significantly. Given these limitations, current control methods in autonomous robots and systems are insufficient to solve the above problems. Therefore, there is an urgent need to study the unique characteristics, requirements, and constraints of vision-based perception and task-control mechanisms. This is crucial for enhancing the capabilities of autonomous robots in performing diverse tasks more intelligently and efficiently.

Hence, we propose this Special Issue on “Vision-Guided System in Intelligent Autonomous Robots”, focusing on exploring new control technologies that can overcome these challenges.

Dr. Jacky C.K. Chow
Dr. Sheikh Izzal Azid
Dr. Leszek Ambroziak
Guest Editors

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Keywords

  • visual perception
  • navigation
  • vision and control for autonomous robots

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Published Papers (1 paper)

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16 pages, 5555 KiB  
Article
Optimization of a Navigation System for Autonomous Charging of Intelligent Vehicles Based on the Bidirectional A* Algorithm and YOLOv11n Model
by Shengkun Liao, Lei Zhang, Yunli He, Junhui Zhang and Jinxu Sun
Sensors 2025, 25(15), 4577; https://doi.org/10.3390/s25154577 - 24 Jul 2025
Viewed by 324
Abstract
Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the [...] Read more.
Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article
(This article belongs to the Special Issue Vision-Guided System in Intelligent Autonomous Robots)
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