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High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future.
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http://dx.doi.org/10.3390/s25072209 | DOI Listing |
Sci Adv
September 2025
The Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, China.
Mobile robots that simultaneously have fast speeds, sufficient load-carrying capabilities, and multiple locomotive functions have always been challenging to develop. Here, we introduce a liquid-amplified electrostatic rolling (LAER) mechanism, which elegantly integrates actuation and adhesion into a streamline single-degree-of-freedom structure. Based on this, we developed a rigid tethered LAER roller (0.
View Article and Find Full Text PDFPLoS One
September 2025
Sanjiang Institute of Artificial Intelligence and Robotics, Yibin University, Sichuan, China.
Fruit detection using the YOLO framework has fostered fruit yield prediction, fruit harvesting automation, fruit quality control, fruit supply chain efficiency, smart fruit farming, labor cost reduction, and consumer convenience. Nevertheless, the factors that affect fruit detectors, such as occlusion, illumination, target dense status, etc., including performance attributes like low accuracy, low speed, and high computation costs, still remain a significant challenge.
View Article and Find Full Text PDFJ Robot Surg
September 2025
Department of Ultrasound, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 1, Minde Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China.
Technological innovations in robot-assisted ultrasound (RAUS) have remarkably advanced the development of precision and intelligent medical imaging diagnosis. This study aims to use bibliometric methods to systematically analyze the technological evolution and research frontiers in the RAUS field, providing valuable insights for future research. This study used the Web of Science Core Collection database to retrieve English-language research papers and reviews related to RAUS published between 2000 and 2024.
View Article and Find Full Text PDFFront Plant Sci
August 2025
School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou, China.
With the development of smart agriculture, fruit picking robots have attracted widespread attention as one of the key technologies to improve agricultural productivity. Visual perception technology plays a crucial role in fruit picking robots, involving precise fruit identification, localization, and grasping operations. This paper reviews the research progress in the visual perception technology for fruit picking robots, focusing on key technologies such as camera types used in picking robots, object detection techniques, picking point recognition and localization, active vision, and visual servoing.
View Article and Find Full Text PDFSci Data
September 2025
Department of Mechanical Convergence Engineering, Hanyang University, 222 Wangsimni-ri, Seongdong-gu, Seoul, 04763, Republic of Korea.
This study provides a comprehensive outdoor ultra-wideband (UWB) dataset to examine the multipath effects in line-of-sight and non-line-of-sight (NLOS) environments for real-time localization. Specifically, the dataset comprises static and dynamic datasets designed to capture discrete multipaths affected by antenna height, obstructions, and time-varying environments. A static dataset varies the antenna height and distance to analyze the multipath interference on the received signal strength and ranging error with a UWB pair and walls to replicate NLOS environments.
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