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Intelligent Mobile Robotics: Object Recognition, Human–Robot Interaction and Autonomous Navigation

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 657

Special Issue Editor


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Guest Editor
Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland
Interests: mobile robots; deep learning; object recognition; object tracking; human motion tracking; human body pose estimation; particle swarm optimization; gait recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mobile robotics is among the most active research topics in modern science. This is due to the potential of mobile robotics to be applied in a wide spectrum of areas, such as autonomous systems, surveillance systems, healthcare, and human–robot interaction. Research on autonomous mobile robots concerns their use in the detection, tracking, and recognition of objects based on data recorded by various types of sensors. These studies employ RGB cameras, depth cameras, LiDAR systems, and IMU sensors and are aimed at developing methods for navigation, localization, and scene analysis. These represent challenging but promising research problems, especially if only visual data are used. Therefore, we welcome high-quality publications on mobile robotics and related topics such as object recognition, scene recognition, and human–robot interaction. More precisely, topics of interest include (but are not limited to) the following:

  • Mobile robots;
  • Autonomous mobile robots;
  • Object recognition;
  • Object detection;
  • Object tracking;
  • Person detection;
  • Person re-identification;
  • Human pose estimation;
  • Human action recognition;
  • Human–robot interaction;
  • Gesture recognition;
  • Place recognition;
  • Scene analysis;
  • Anomaly detection;
  • Multi-sensor analysis;
  • Navigation and localization;
  • Camera pose estimation;
  • Simultaneous localization and mapping;
  • Application of computer vision methods for mobile robotics (e.g., autonomous systems, surveillance, and transport and storage).

Dr. Tomasz Krzeszowski
Guest Editor

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Keywords

  • mobile robot
  • object recognition
  • object detection
  • object tracking
  • place recognition
  • navigation
  • localization
  • SLAM

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Published Papers (1 paper)

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Research

19 pages, 13879 KB  
Article
RGB-Based Staircase Detection for Quadrupedal Robots: Implementation and Analysis
by Piotr Wozniak, Paweł Penar and Damian Bielecki
Sensors 2025, 25(23), 7247; https://doi.org/10.3390/s25237247 - 27 Nov 2025
Viewed by 496
Abstract
In this paper, we present a stair detection algorithm verified on various platforms, including a real quadruped robot. The proposed approach detects dangerous situations, such as proximity to stairs, in real time, enabling the robot to move safely. In our research, we utilized [...] Read more.
In this paper, we present a stair detection algorithm verified on various platforms, including a real quadruped robot. The proposed approach detects dangerous situations, such as proximity to stairs, in real time, enabling the robot to move safely. In our research, we utilized data collected by a moving four-legged robot, recorded in 18 sequences containing more than 26,000 color images from two cameras positioned at different perspectives. To address the challenge, we utilize a deep neural network with RGB inputs for object detection, complemented by preprocessing, and post-processing. A key feature of this approach is its adaptability to varying camera views, including both front and bottom perspectives on the robot, with training that incorporates multi-camera images from both views. We implemented and tested this algorithm on the Unitree Go1 robot, as well as on other embedded platforms. Using the trained YOLO version 11n network on a single sequence and testing on 17 sequences, we achieved an average mAP@50 of 51.30 for images containing only stairs and 87.56 for all images. This method enables early hazard detection during stair navigation. The proposed evaluation scenario tests the model’s adaptation from a single training sequence to multiple unseen sequences, extending existing stair detection methods for quadrupedal robots. The dataset presents high variability in stair appearance due to the robot’s perspective and limited real-time processing capacity. Full article
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