Machine learning approach to self-localization of mobile robots using RFID tag

Senta, Yosuke; Kimuro, Yoshihiko; Takarabe, Shinya; Hasegawa, Tsutomu

doi:10.1109/aim.2007.4412485

Cited by 12 publications

(3 citation statements)

References 3 publications

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“…Companies like Confidex have specialized in developing RFID tags designed to withstand chemicals, washing, and high temperatures during the manufacturing process, further automating logistics [76]. Despite the advantages of RFID, challenges remain, including malfunctioning tags and the time-consuming creation and maintenance of look-up tables with geo-positions [77]. Here, computer vision offers a valuable solution as it serves as the primary sensory system for both navigation and load detection [78].…”

Section: Robot-driven Logisticsmentioning

confidence: 99%

A Systematic Literature Review on the Application of Automation in Logistics

Ferreira,

Reis

2023

Logistics

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Background: in recent years, automation has emerged as a hot topic, showcasing its capacity to perform tasks independently, without constant supervision. While automation has witnessed substantial growth in various sectors like engineering and medicine, the logistics industry has yet to witness an equivalent surge in research and implementation. Therefore, it becomes imperative to explore the application of automation in logistics. Methods: this article aims to provide a systematic analysis of the scientific literature concerning artificial intelligence (AI) and automation in logistics, laying the groundwork for robust and relevant advancements in the field. Results: the foundation of automation lies in cutting-edge technologies such as AI, machine learning, and deep learning, enabling self-problem resolution and autonomous task execution, reducing the reliance on human labor. Consequently, the implementation of smart logistics through automation has the potential to enhance competitiveness and minimize the margin of error. The impact of AI and robot-driven logistics on automation in logistics is profound. Through collaborative efforts in human–robot integration (HRI), there emerges an opportunity to develop social service robots that coexist harmoniously with humans. This integration can lead to a revolutionary transformation in logistics operations. By exploring the scientific literature on AI and automation in logistics, this article seeks to unravel critical insights into the practical application of automation, thus bridging the existing research gap in the logistics industry. Conclusions: the findings underscore the impact of artificial intelligence and robot-driven logistics on improving operational efficiency, reducing errors, and enhancing competitiveness. The research also provided valuable insights into the applications of various automation techniques, including machine learning and deep learning, in the logistics domain. Hence, the study’s insights can guide practitioners and decision makers in implementing effective automation strategies, thereby improving overall performance and adaptability in the dynamic logistics landscape. Understanding these foundations can pave the way for a future where automation and human expertise work hand in hand to drive logistics toward unparalleled efficiency and success.

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Section: Robot-driven Logisticsmentioning

confidence: 99%

A Systematic Literature Review on the Application of Automation in Logistics

Ferreira,

Reis

2023

Logistics

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“…In brief, extracted curvatures are derived from segmented range images from a laser rangefinder. These segmented According to the "Machine learning approach to self-localization of mobile robots using RFID tag" [12] by Senta et al, a different approach from the previously mentioned article for RFID landmark localization is proposed. Instead of determining position and orientation by solving the kinematic or geometric problem, this research applying the machine learning approach, namely the support vector machine (SVM), to avoid some difficulties, e.g., define every tag's position, complex kinematic problem.…”

Section: Natural Landmark-based Localizationmentioning

confidence: 99%

“…Also, the mechanical defect can cause the steadystate error. The integral controller, shown by Equation (12), can manage to eliminate this type of error. According to Equation ( 12), the integral controller can output the non-zero control signal even when the error is zero, which in case of the proportional controller will give a zero output.…”

Section: Integral Controllermentioning

confidence: 99%

Path following and obstacle avoidance for autonomous vehicle based on gnss localization

Kiataramgul

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Nowadays, many systems, formerly operated by human beings, are now developed to minimize user control effort. One outstanding system that receives close attention is the autonomous driving system. However, several autonomous driving systems that are currently developed utilize expensive sensing devices, e.g., camera and laser scanner. Moreover, these devices cannot be solely employed but a high-performance processing unit is also required. These pricey components result in an expensive system that does not worth to be used in some practical applications. Therefore, this research intends to utilize other low-cost sensing devices so that the final price of the developed system can be reduced. Hence, the Global Navigation Satellite System (GNSS) with a Real-Time Kinematic (RTK) correction was applied to this research as a prior sensor. However, a laser scanner was still employed as a complementary sensor to detect obstacles which cannot be detected by the GNSS alone. This developed system was designed to effectively operate at a travel speed lower than 15 kilometers per hour in a GNSS-friendly environment. In this research, the micro electric vehicle was modified by installing the steering control system and the speed control system, which consists of the acceleration control system and the braking control system. These supplementary systems are controlled by the high-level control system. Next, the high-level control system software was developed. This software controls a vehicle to follow a predefined route by using the GNSS in a localization process and using a laser scanner in the obstacle avoidance algorithm which was developed in this research, i.e., the Scored Predicted Trajectory. Then, the parameters, which affect the high and low-level control system characteristic, was tuned until a satisfactory response was achieved. Next, the developed autonomous navigation system evaluation experiment was conducted in a controlled environment area by separately evaluated the path following system and the obstacle avoidance system. After the path following system experiment was launched using a travel speed of 10 and 15 kilometers per hour, it has been found that the developed system effectively performs even some portions of the test track are either covered by large trees or surrounded by buildings, i.e., the environment by which the performance of the GNSS is degraded. The result of this experiment shows the average deviation distance from the waypoint of about 10 centimeters. In the obstacle avoidance system experiment, the result shows that the developed system responds to the obstacle by evading it and safely converging to the predefined path according to the designed algorithm.

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