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

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Navigation and Positioning".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 11842

Editor

Dr. Tomasz Krzeszowski

E-Mail Website
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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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

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

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Published Papers (6 papers)

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Research

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25 pages, 1925 KB  
Article
People Counting Using YOLO-Based Detection and Clustering for a Mobile Robot
by Kamil Gomulka, Piotr Wozniak and Tomasz Krzeszowski
Sensors 2026, 26(15), 4664; https://doi.org/10.3390/s26154664 - 23 Jul 2026
Viewed by 738
Abstract
People counting is one of the key tasks in intelligent monitoring systems. However, accurately counting people in dynamic environments can be extremely challenging. This is especially true in mobile robot applications, where challenges such as a moving camera, varying conditions, and a limited [...] Read more.
People counting is one of the key tasks in intelligent monitoring systems. However, accurately counting people in dynamic environments can be extremely challenging. This is especially true in mobile robot applications, where challenges such as a moving camera, varying conditions, and a limited field of view due to environmental obstacles arise. In such scenarios, the people counting task primarily involves visually detecting and grouping individuals to determine the total number of unique people. This paper presents a people counting algorithm based on visual people detection and clustering. The method utilizes the You Only Look Once (YOLO) detector to identify the bounding boxes of detected individuals and extract features from their corresponding regions of interest (ROIs). Additionally, the dimension of the extracted features is reduced using an encoder and clustered to distinguish individuals, with the number of clusters serving as an estimate of the number of people. The method was tested on a dataset containing 45 independent sequences with a total of 19,350 RGB images, complete with metadata for people detection and re-identification. This dataset encompasses various settings, particularly scenarios featuring mobile robots moving and capturing frames in indoor environments. The experiments demonstrate the effectiveness of the proposed method in different configurations. The best results were achieved using the YOLOv10n detector combined with K-means or SK-means clustering, yielding a Mean Absolute Error (MAE) of 1.11. The proposed encoder-based method significantly reduces clustering time and operates effectively within the limited resources available on mobile robotic platforms such as the Jetson Nano. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotics: Object Recognition, Human–Robot Interaction and Autonomous Navigation)
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25 pages, 895 KB  
Article
Poisson Multi-Bernoulli Filter Driven Information-Controlled Selection of Pose Graph Constraints for SLAM
by Tao Li, Ying Hu, Zijing Zhang and Fei Zhang
Sensors 2026, 26(13), 4138; https://doi.org/10.3390/s26134138 - 1 Jul 2026
Viewed by 404
Abstract
Traditional SLAM methods face significant challenges in complex environments, including high computational complexity, ambiguous data association, and limited real-time performance. Existing approaches often rely on explicit data association or computationally intensive filtering frameworks, which restrict their scalability and robustness. In this paper, we [...] Read more.
Traditional SLAM methods face significant challenges in complex environments, including high computational complexity, ambiguous data association, and limited real-time performance. Existing approaches often rely on explicit data association or computationally intensive filtering frameworks, which restrict their scalability and robustness. In this paper, we propose a pose-graph-optimization-based Poisson multi-Bernoulli (PMB) SLAM framework. The proposed method models the map as a unified structure consisting of undetected features represented by a Poisson point process (PPP) and detected features represented by multi-Bernoulli (MB) components, enabling consistent feature estimation while reducing the reliance on explicit data association. Furthermore, an information-controlled pose graph constraint selection strategy (IC-PGCS) is developed to effectively couple PMB filtering with pose graph optimization, allowing adaptive activation of graph optimization based on accumulated information. Simulation results demonstrate that the proposed method achieves comparable map feature estimation accuracy while improving computational efficiency and real-time performance compared with RB-PHD-SLAM and multi-Bernoulli-based SLAM methods. These results validate the effectiveness of the proposed framework for SLAM applications in cluttered indoor environments. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotics: Object Recognition, Human–Robot Interaction and Autonomous Navigation)
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26 pages, 5908 KB  
Article
A2PM-VINS: A Visual–Inertial SLAM Method Based on Area-to-Point Matching
by Mengxing Ma, Zengao Jiang, Yunhai Yan, Jianing Tang and Yunhao Chen
Sensors 2026, 26(10), 3071; https://doi.org/10.3390/s26103071 - 13 May 2026
Viewed by 657
Abstract
The localization performance of visual–inertial simultaneous localization and mapping (VI-SLAM) strongly depends on front-end feature matching. In degraded scenes with low illumination, repetitive textures, and weak textures, traditional geometric front ends often suffer from sparse features and mismatches, resulting in unstable state estimation. [...] Read more.
The localization performance of visual–inertial simultaneous localization and mapping (VI-SLAM) strongly depends on front-end feature matching. In degraded scenes with low illumination, repetitive textures, and weak textures, traditional geometric front ends often suffer from sparse features and mismatches, resulting in unstable state estimation. To address this issue, this paper proposes Area-to-Point Matching Visual–Inertial SLAM (A2PM-VINS), a visual–inertial SLAM method based on Area-to-Point matching. The method introduces Area-to-Point hierarchical matching and a kinematic temporal inheritance mechanism to improve matching reliability and track continuity, and further designs an Anchor–Explorer feature selection strategy to retain features with higher geometric value for back-end optimization. In addition, a Sub-Window Consistency (SWC) weighting strategy is incorporated into the back end to suppress geometrically deceptive observations with poor temporal continuity and geometric consistency. Experiments on the European Robotics Challenge Micro Aerial Vehicle (EuRoC MAV) dataset show that A2PM-VINS achieves superior or competitive localization accuracy on multiple challenging sequences. The absolute trajectory errors on MH_04 and MH_05 are 0.0983 m and 0.1191 m, respectively, and stable tracking is maintained on V2_02, where VINS-Fusion fails. These results show that the proposed method effectively improves the robustness of visual–inertial state estimation in complex degraded environments. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotics: Object Recognition, Human–Robot Interaction and Autonomous Navigation)
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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 1516
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
(This article belongs to the Special Issue Intelligent Mobile Robotics: Object Recognition, Human–Robot Interaction and Autonomous Navigation)
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Review

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21 pages, 1001 KB  
Review
Recent Developments and Applications of Drone Swarm: Techniques, Strategies, and Challenges
by Ravi Raj and Andrzej Kos
Sensors 2026, 26(10), 2943; https://doi.org/10.3390/s26102943 - 8 May 2026
Cited by 3 | Viewed by 6238
Abstract
The dynamic and complex environment, together with challenging assignments, requires that unmanned aerial vehicle (UAV) systems evolve toward cooperation, autonomy, and cognition. UAV swarms illustrate a revolutionary development in aerial robotics, which utilizes coordinated autonomy to improve operational efficiency. This study offers a [...] Read more.
The dynamic and complex environment, together with challenging assignments, requires that unmanned aerial vehicle (UAV) systems evolve toward cooperation, autonomy, and cognition. UAV swarms illustrate a revolutionary development in aerial robotics, which utilizes coordinated autonomy to improve operational efficiency. This study offers a detailed examination of UAV swarm systems, the latest developments, and their different applications. The main domains, such as intelligent path planning, work allocation, coordinated control, and safety issues, are analyzed, focusing on the integration of Artificial Intelligence (AI) and Deep Learning (DL) to enhance decision-making and agility. We address the constraints and potential advances in the field of swarm intelligence to facilitate additional research endeavors. The ongoing advancement of drone swarm technologies and its exploration of military uses highlight the increasing importance of anti-drone swarm strategies. Therefore, studying these strategies will have substantial practical importance in preventing and countering drone swarm combat. Thus, this article provides detailed drone swarm applications and the importance of anti-drone swarm techniques in strategic operations. Furthermore, this comprehensive study of the literature aims to offer innovative perspectives on the latest advances in UAV swarm intelligence technology. Future research trends and challenges are discussed to find the research gap. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotics: Object Recognition, Human–Robot Interaction and Autonomous Navigation)
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58 pages, 7331 KB  
Review
Human–Robot Interaction in Indoor Mobile Robotics: Current State, Interaction Modalities, Applications, and Future Challenges
by Arman Ahmed Khan and Kerstin Thurow
Sensors 2026, 26(6), 1840; https://doi.org/10.3390/s26061840 - 14 Mar 2026
Cited by 1 | Viewed by 1452
Abstract
This paper provides a comprehensive survey of Human–Robot Interaction (HRI) for indoor mobile robots operating in human-centered environments such as hospitals, laboratories, offices, and homes. We review interaction modalities—including speech, gesture, touch, visual, and multimodal interfaces—and examine key user experience factors such as [...] Read more.
This paper provides a comprehensive survey of Human–Robot Interaction (HRI) for indoor mobile robots operating in human-centered environments such as hospitals, laboratories, offices, and homes. We review interaction modalities—including speech, gesture, touch, visual, and multimodal interfaces—and examine key user experience factors such as usability, trust, and social acceptance. Implementation challenges are discussed, encompassing safety, privacy, and regulatory considerations. Representative case studies, including healthcare and domestic platforms, highlight design trade-offs and integration lessons. We identify critical technical challenges, including robust perception, reliable multimodal fusion, navigation in dynamic spaces, and constraints on computation and power. Finally, we outline future directions, including embodied AI, adaptive context-aware interactions, and standards for safety and data protection. This survey aims to guide the development of indoor mobile robots capable of collaborating with humans naturally, safely, and effectively. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotics: Object Recognition, Human–Robot Interaction and Autonomous Navigation)
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