An idea to amalgamate GPS and GLONASS to form the best part of GNSS tracking system for mobile robot navigation. The introduction of GNSS made additional satellites visible to localize Mobile robot accurately in both indoor and outdoor application; hence the localization accuracy will be increased in the non-line of sight areas of GPS satellites. This paper explains about an antenna which operates in GPS-1.575 GHz and GLONASS-1.602 GHz system frequency bands. A single layer square patch antenna with size of 45 mm * 45 mm is designed and simulated using Computer Simulation Technology Algorithmic model of the proposed antenna is derived and RLC values of equivalent circuit are identified using MATLAB program. The prototype antenna is fabricated and tested using network analyser for observing the experimental results. The Proposed antenna parameters are compared with simulation, theoretical and existing antenna results. Thus, the findings support that the proposed antenna system could be useful for the localization of car like mobile robot in indoor and outdoor environments.
Autonomous transportation is a new paradigm of an Industry 5.0 cyber-physical system that provides a lot of opportunities in smart logistics applications. The safety and reliability of deep learning-driven systems are still a question under research. The safety of an autonomous guided vehicle is dependent on the proper selection of sensors and the transmission of reflex data. Several academics worked on sensor-based difficulties by developing a sensor correction system and fine-tuning algorithms to regulate the system’s efficiency and precision. In this paper, the introduction of vision sensor and its scene terrain classification using a deep learning algorithm is performed with proposed datasets during sensor failure conditions. The proposed classification technique is to identify the mobile robot obstacle and obstacle-free path for smart logistic vehicle application. To analyze the information from the acquired image datasets, the proposed classification algorithm employs segmentation techniques. The analysis of proposed dataset is validated with U-shaped convolutional network (U-Net) architecture and region-based convolutional neural network (Mask R-CNN) architecture model. Based on the results, the selection of 1400 raw image datasets is trained and validated using semantic segmentation classifier models. For various terrain dataset clusters, the Mask R-CNN classifier model method has the highest model accuracy of 93%, that is, 23% higher than the U-Net classifier model algorithm, which has the lowest model accuracy nearly 70%. As a result, the suggested Mask R-CNN technique has a significant potential of being used in autonomous vehicle applications.
Over the past decades, the development of engineering skills among students has been drastically reduced in Indian education. The students need to develop not only knowledge but also the necessary skills to survive in fastgrowing technology. Particularly in industry-related automation courses, the fourth industrial revolution (Industry 4.0) adds to the necessity of building engineering skills at a higher level. It is well observed from various educational studies that engineering skills with higher cognitive ability can be developed with problem-based learning (PBL). The curriculum has to be designed in such a way that it provides a flexible framework for PBL. As the current assessment system has written exams as a major element of evaluation, PBL can be incorporated into practice in the form of hands-on problem-based assignments (PBA) that are evaluated periodically. This study proposes the exercising of PBL as PBA to validate K4 (Analyze) and K5 (Evaluate) cognitive levels according to Bloom's cognitive domain. The proposed case study discusses the impact of project-based assignment (PBA) in two different approaches in the curriculum of Mechatronics Engineering.The first approach follows Outcome Based Education with Choice Based Credit System (OBE-CBCS approach) and the second approach has an additional Conceive Design Implement and Operate framework to it (OBE-CBCS-CDIO approach). The continuous assessment and assignment methods vary with different education curriculum systems. This study presents the impact study of the PBA activity in a Mechatronics course, "Computer Numerical Control (CNC) Application" in 2 consecutive years with two different curriculum frameworks. The results show that the OBE-CBCS-CDIO framework students outperform the OBE-CBCS students in acquiring higher-order thinking skills.
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