<abstract>
<title style='display:none'>Abstract</title>
This paper presents a method for automatically measuring plants’ heights in indoor hydroponic plantations using the OpenCV library and the Python programming language. Using the elaborated algorithm and Raspberry Pi-driven system with an external camera, the growth process of multiple pak choi cabbages (Brassica rapa L. subsp. Chinensis) was observed. The main aim and novelty of the presented research is the elaborated algorithm, which allows for observing the plants’ height in hydroponic stations, where reflective foil is used. Based on the pictures of the hydroponic plantation, the bases of the plants, their reflections, and plants themselves were separated. Finally, the algorithm was used for estimating the plants’ heights. The achieved results were then compared to the results obtained manually. With the help of a ML (Machine Learning) approach, the algorithm will be used in future research to optimize the plants’ growth in indoor hydroponic plantations.
</abstract>

Application of the OpenCV library in indoor hydroponic plantations for automatic height assessment of plants

<abstract>
<title style='display:none'>Abstract</title>
The paper presents a design for a six-axis manipulator. The design consists of specially designed solutions for housing, planetary gearboxes, and electronics. The manipulator is controlled by a supervisory control system. The use of a series of measuring elements allows tracking of the current position of each axis. This can be used to create a cascade control loop with velocity and acceleration feed-forward. The implemented control algorithm together with the microcontroller software allows one to tune parameters of the controllers for both control loops: inner, related to the speed of the robot; outer, related to its position.
</abstract>

Prototype and Design of Six-Axis Robotic Manipulator

<abstract>
<title style='display:none'>Abstract</title>
In this paper, the processing of the data of a 3D light detection and distance measurement (LiDAR) sensor mounted on a mobile robot is demonstrated, introducing an innovative methodology to manage the data and extract useful information. The LiDAR sensor is placed on a mobile robot which has a modular design that permits the easy change of the number of wheels, was designed to travel through several environments, and saves energy by changing the number and arrangement of the wheels in each environment. In addition, the robot can recognize landmarks in a structured environment by using a classification technique on each frame acquired by the LiDAR. Furthermore, considering the experimental tests, a new simple algorithm based on the LiDAR data processing together with the inertial data (IMU sensor) through a Kalman filter is proposed to characterize the robot’s pose by the surrounding environment with fixed landmarks. Finally, the limits of the proposed algorithm have been analyzed, highlighting new improvements in the future prospective development for permitting autonomous navigation and environment perception with a simple, modular, and low-cost device.
</abstract>

Processing of LiDAR and IMU Data for Target Detection and Odometry of a Mobile Robot

<abstract>
<title style='display:none'>Abstract</title>
In alignment with smart city initiatives, Malaysia is shifting its educational landscape to a smart learning environment. The Ministry of Education (MoE) has made History a mandatory subject for passing the Malaysian Certificate of Education to grow awareness and instil patriotism among Malaysian students. However, History has been known as one of the difficult subjects to study for many students. On the other hand, the Malaysian Government Education Blueprint 2013-2025 seeks to “leverage ICT scale up quality learning” across the country. Therefore, this study aims to identify the factors that influence the intention to use Augmented Reality (AR) for mobile learning in learning History subject among secondary school students in Malaysia. Quantitative approach has been chosen as the research method for this study. A direct survey was conducted on 400 secondary school students in one of the smart cities in Malaysia as the target respondents. The collected data are analysed through descriptive statistics and Multiple Linear Regression analysis by using Statistical Package for the Social Sciences. Based on the results, the identified factors that influence the intention to use AR for mobile learning in learning History subject are Gender, Perceived Usefulness, Perceived Enjoyment, and Attitude Towards Use. The identified factors can be a good reference for schools and teachers to strategize their teaching and learning methods in pertaining to History subject among secondary school students in Malaysia. Future studies may include the study of various types of schools in Malaysia and explore more moderating effects of demographic factors.
</abstract>

Technology Acceptance in Learning History Subject Using Augmented Reality Towards Smart Mobile Learning Environment: Case in Malaysia

<abstract>
<title style='display:none'>Abstract</title>
This paper presents the development of an automated guided vehicle with omni-wheels for autonomous navigation under a robot operating system framework. Specifically, a laser rangefinder-constructed two-dimensional environment map is integrated with a three-dimensional point cloud map to achieve real-time robot positioning, using the oriented features from accelerated segment testing and a rotated binary robust independent elementary feature detector-simultaneous localization and mapping algorithm. In the path planning for autonomous navigation of the omnidirectional mobile robot, we applied the A* global path search algorithm, which uses a heuristic function to estimate the robot position difference and searches for the best direction. Moreover, we employed the time-elastic-band method for local path planning, which merges the time interval of two locations to realize time optimization for dynamic obstacle avoidance. The experimental results verified the effectiveness of the applied algorithms for the omni-wheeled mobile robot. Furthermore, the results showed a superior performance over the adaptive Monte Carlo localization for robot localization and dynamic window approach for local path planning.
</abstract>

Development of an Omnidirectional AGV by Applying ORB-SLAM for Navigation Under ROS Framework

<abstract>
<title style='display:none'>Abstract</title>
The rapid development of robotics involves human-robot interaction (HRI). It is a necessary to assess user satisfaction to develop HRI effectively. Thus, HRI calls for interdisciplinary research, including psychological instruments such as survey questionnaire design. Here, we present a factor analysis of a Polish version of the Godspeed Questionnaire (GSQ) used to measure user satisfaction. The questionnaire was administered to 195 participants. Then, factor analysis of the GSQ was performed. Finally, reliability analysis of the Polish version of the GSQ was done. The adapted version of the survey was characterized by a four-factor structure, i.e., anthropomorphism, perceived intelligence, likeability, and perceived safety, with good psychometric properties.
</abstract>

Factor Analysis of the Polish Version of Godspeed Questionnaire (GQS)

<abstract>
<title style='display:none'>Abstract</title>
Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. Model-based predictive control (MPC) algorithms with nonlinear state-space models and most recent disturbance attenuation technique are proposed. This technique makes controller design and online calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison, a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is investigation of influence of sampling period and of computational delay time on performance of the algorithms, which is practically important when using model-based algorithms with fast sampling.
</abstract>

Effective nonlinear Predictive and CTC-PID Control of Rigid Manipulators

<abstract>
<title style='display:none'>Abstract</title>
The design method and the time-variant finite impulse response (FIR) architecture for real-time estimation of fractional and integer differentials and integrals are presented in this paper. The proposed FIR architecture is divided into two parts. Small-phase filtering, integer differentiation, and fractional differential and integration on the local data are performed by the first part, which is time-invariant. The second part, which is time-variant, handles fractional and global differentiation and integration. The separation of the two parts is necessary because real-time matrix inversion or an extensive analytical solution, which can be computationally intensive for high-order FIR architectures, would be required by a single time-variant FIR architecture. However, matrix inversion is used in the design method to achieve negligible delay in the filtered, differentiated, and integrated signals. The optimum output obtained by the method of least squares results in the negligible delay. The experimental results show that fractional and integer differentiation and integration can be performed by the proposed solution, although the fractional differentiation and integration process is sensitive to the noise and limited resolution of the measurements. In systems that require closed-loop control, disturbance observation, and real-time identification of model parameters, this solution can be implemented.
</abstract>

Design of Small-Phase Time-Variant Low-Pass Digital Fractional Differentiators and Integrators

<abstract>
<title style='display:none'>Abstract</title>
This article presents research results on building a model to reproduce ship vibrations based on a parallel robot with 6 degrees of freedom on the Gough-Stewart platform. Vibration data at the ship’s center of gravity, calculated by simulation software, will be inputted into the model. The regenerative control system uses a simple PID controller to control input trajectory tracking. Simulation results on Matlab/Simulink software have demonstrated the reproduction of ship vibrations within the allowable error.
</abstract>

Research to Simulate the Ship’s Vibration Regeneration System using A 6-Degree Freedom Gough-Stewart Parallel Robot

<abstract>
<title style='display:none'>Abstract</title>
In this paper, we proposed a comparative research project on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-19, ResNet-50, Inception-V3, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above-mentioned pre-trained models of CNN and R-CNN to achieve optimal results for accuracy as well as productivity in detection of various objects such as ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, a convolutional neural network (CNN) is used as the base model. For complex computations and for speeding up results, transfer learning is used. With the help of experimental analysis, we have discovered that R-CNN and Inception-V3 performed best out of the five pre-trained models.
</abstract>

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on Dota

<abstract>
<title style='display:none'>Abstract</title>
With the rapid advancements in technology, the educational landscape is witnessing significant transformations in pedagogy and classroom dynamics. Two prominent technologies, Artificial Intelligence (AI) and Augmented Reality (AR), are gaining prominence in the field of education, promising to revolutionize the way teaching and learning take place. This article explores the potential benefits, challenges, and practical applications of integrating AI and AR into the teaching process to enhance student engagement and learning outcomes.
The integration of AI in education brings forth personalized learning experiences. AI-powered algorithms analyze vast amounts of student data, including learning patterns, strengths, and weaknesses, to create tailored learning paths. This individualized approach helps educators identify students’ unique needs and provide targeted support, ensuring that no student is left behind. Moreover, AI-based chatbots and virtual teaching assistants are increasingly being used to address student queries promptly, providing real-time support and fostering a more interactive learning environment.
AR, on the other hand, enables the overlay of virtual objects and information in the real-world environment.. Students can explore complex concepts through visualizations, simulations, and interactive demonstrations, facilitating a deeper understanding of abstract topics. AR also fosters collaboration and teamwork among students, promoting active learning and peer-to-peer knowledge sharing.
Combining AI and AR technologies offers a powerful synergy in the educational realm. AI can analyze ARgenerated data and adapt instructional strategies in real time, responding to individual students’ progress. This synergy not only enhances learning outcomes but also empowers teachers with data-driven insights, enabling them to make informed decisions about their teaching methodologies.
However, successfully implementing AI and AR in education comes with its challenges. Issues related to data privacy, ethical considerations, and the need for effective teacher training in utilizing these technologies require careful attention. Additionally, the digital divide can exacerbate educational inequalities, as not all students have equal access to technology outside the classroom.
Collaboration between educators, researchers, and technology developers is crucial to overcome these challenges. The development of user-friendly, accessible, and ethically sound AI and AR tools can ensure inclusivity and maximize the potential benefits of these technologies in education. The aim is to investigate whether the use of AR technology can enhance students’ understanding and mastery of physics concepts through visualizations and simulations. Spatial intelligence plays a crucial role in various subjects, including physics, as it enables students to create mental models and representations of objects and expressions. While spatial intelligence is not an innate skill, it can be developed through interactions with real and virtual objects. ARas a cutting-edge technology, has the potential to illustrate physical applications and significantly aid students in visualizing and comprehending complex physics concepts.
</abstract>

Improving Teaching Using Artificial Intelligence and Augmented Reality

<abstract>
<title style='display:none'>Abstract</title>
Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables the delegating of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support the CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows encountered limitations to be overcome and the flexibility known from high-level solutions and high performance achieved with low-level programming to be maintained without the need for a high financial background.
</abstract>

SOC-FPGA Based Concept of Hardware Aided Quantum Simulation

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Journal of Automation, Mobile Robotics and Intelligent Systems (ISSN 1897-8649 (Print) / ISSN 2080-2145 (Online) publishes high-quality, state-of-the-art technical...

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