The focus of this research is to propose a method known as position-based contact force synchronous control for cooperative manipulation of an object using a dual-arm manipulator. By defining relative positional and force errors between both manipulators and the object, these errors can be utilized in conjunction with a general impedance controller to enhance contact force accuracy during operation. This unique approach guarantees coordination between both arms while minimizing internal forces exerted on the object compared to conventional impedance control techniques.

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Recently, Autonomous Underwater Vehicles: AUVs are utilized as the tools for ocean survey and practical applications such as ocean mineral resources survey and marine biological investigations. We have developed an AUV “Tuna-Sand2” and have succeeded in automatic sampling of shells in sea trials at Suruga Bay in Japan. Tuna-Sand2 is designed for 2000m depth and 8 hours operation with the speed of 1 knot and has several computers for basic motion control with sensor handling, intelligent behaviors based on image processing and data transmission, however, the robot needs efforts in deployment and recovery because of weight and sizes, and must return to the surface in emergency conditions. That is, the system should be stable, reliable and conservative, and not suitable for testing new challenging algorithms and behaviors. We have developed a new AUV KYUBIC which can be operated by a few people as a small testbed of Tuna-Sand2 and have similar shape and thruster arrangement. In this paper, we describe the system architecture of KYUBIC and the experimental results in Underwater Robotics Competition in Okinawa 2020.

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This study aimed to reduce the effect of the examining environment on accuracy by using an eye movement detection system, a VR head-mounted display. We reproduced the inspection environment in a virtual reality environment and performed the cover test, a basic inspection technique for tropia and phoria. We then developed a system that uses eye data collected by eye tracking to detect the directions and magnitudes of eye misalignment. The Maddox method, an existing testing procedure, was used to verify the accuracy. We have confirmed its effectiveness in detecting the directions and magnitudes of horizontal eye misalignment.

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Compared to mobile robots equipped with multiple rollers, spherical robots can move in all directions and are superior in durability and in their ability to climb steps. Slippage between balls and rollers is a significant problem in friction drives. However, previously established roller-driven ball kinematics model considers sliding on only two constraining rollers. In this research, we developed it, proposed a motion model of sphere with three-constraint rollers, and developed a mathematical model that simulates the angular velocity vector of the sphere and the slip vector at each contact point. And we considered the existence of an angular velocity vector of sphere adapted three constraint rollers from the viewpoint of forward kinematics and succeed demonstration of the trajectory of the endpoint of the angular velocity vector and slip velocity vector of sphere.

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This research paper presents a novel approach for accurate weight estimation in robotic manipulation of noodle-like objects. The proposed approach combines vision transformer and autoencoder techniques with action data and RGB-D encoding to enhance the capabilities of robots in manipulating objects with varying weights. A deep-learning neural network is introduced to estimate the grasping action of a robot for picking up noodle-like objects using RGB-D camera input, a 6-finger gripper, and Cartesian movement. The hardware setup and characteristics of the noodle-like objects are described. The study builds upon previous work in RGB-D perception, weight estimation, and deep learning, addressing the limitations of existing methods by incorporating robot actions. The effectiveness of vision transformers, autoencoders, self-supervised deep reinforcement learning, and deep residual learning in robotic manipulation is discussed. The proposed approach leverages the Transformer network to encode sequential and spatial information for weight estimation. Experimental evaluation on a dataset of 20,000 samples collected from real environments demonstrates the effectiveness and accuracy of the proposed approach in grappling noodle-like objects. This research contributes to advancements in robotic manipulation, enabling robots to manipulate objects with varying weights in real-world scenarios.

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For nearly two decades, many massage machine (MM) manufacturers have developed a lot of distinct types of FMM. Common massage goods on the sale are roller and pressing models. However, stimulating all acupuncture points (AP) is extremely hard for distinct sizes of feet accurately. Besides, the massage roller cannot be manipulated all alone. Thence, the author proposed a novel computer vision skillfulness to make out the foot acupuncture points (FAP) by ANN. First, the sole of users’ soles is captured and image preprocessing procedures are executed to segment the region of interest (ROI) of soles. FAP is mapped to foot images (FI) to obtain reference massage positions. Afterwards, the YCbCr color space is used to part the brightness to get done the segmentation of the FI in the skin detection. To improve the success rate of image segmentation (IS), ANN is used to train plantar image set. Finally, a FMM was redesigned to raise the rate of ID and user convenience. Experimental results confirm the practicality of the proposed ID method for FMM.

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Efficient strategies for achieving comprehensive area coverage are imperative in the context of search and exploration missions carried out by patrolling UAVs. Diverse methodologies pertaining to coverage path planning were meticulously examined and assessed within the ROS-Gazebo simulation environment, utilizing the Hector quadrotor model. While the concept of opting for an optimal coverage-oriented path is captivating, it necessitates a trade-off, as it often demands a higher frequency of maneuvers to effectively encompass the designated region. Furthermore, the typical oversight of the robots' hardware limitations is prevalent. This study undertook an analysis of the interplay between coverage area and coverage path, employing techniques such as raster-scan exploration, expanding spiral searches, and zigzag pattern coverage, all aimed at enhancing the selection of the most appropriate path. Furthermore, this research delved into investigating the implications arising from the hardware limitations intrinsic to the Hector quadrotor UAV when simulated within the ROS environment. The assessment metrics encompassed variables including the proportion of the area covered, the count of executed turns, and the time taken by the UAV to complete the maneuvers. The outcomes strongly advocate for the inclusion of hardware limitations in both path planning and path structure considerations in order to attain optimal outcomes.

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In this research, we introduce an interactive beating drum game built on the Unity game engine. Our objective is to address the social and mental health impacts of the pandemic, particularly among older individuals. Using the Unity engine, we have developed a sports and social platform. And we employed Bluetooth Low Energy (BLE) chip to transmit drumming signals to the Unity engine following the Bluetooth (BT) protocol. Then, the user interface on the tablet in our game will show the drum locations beat by user. Our game offers single player mode and multiplayer mode. The multiplayer mode provides social network features. Players need to strike the falling icons on the drum in conformity with the rhythm of the music. The game will assess precision and give reward points at the end of the game. In accordance with experimental findings, the elderly participants considered that the system is user-friendliness.

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Unmanned aerial system technology is being used more and more frequently in the construction sector today. Over the past ten years, unmanned aerial systems (UAS) have developed into cutting-edge military technology and readily available consumer goods. Unmanned aerial system technology is being used more and more frequently in the construction business today. Drone applications are shifting more and more toward productive remote procedures that can be carried out in a variety of building industry settings. Remote sensing tools for Unmanned Aerial Systems can be used to examine and assess the status of ongoing building construction projects. This research will let building construction professionals see how remote sensing technology may replace human paper-based supervision and an unorganized database for the existing traditional construction job progression method.

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In the nervous system, there is a broad variety of neuron types, each exhibiting distinct firing properties. Although these neurons are considered important, the understanding of their role in information processing remains limited. In this study, we constructed a simple network using a piecewise quadratic neuron (PQN) model that can reproduce a variety of neuronal activities. Further, we examined the effect of various neuronal dynamics on the success rate of a biologically plausible spike-pattern detection task. The simulation results showed that certain mathematical structures increased the success rate of spike-pattern detection.

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Effective rice field management and the proper application of agricultural chemicals are crucial for ensuring agricultural product quality. These chemicals control weeds and protect against insect pests, which can harm crop yields and quality. This research explores the relationship between the altitude at which agricultural drones spray chemicals, spray uniformity, and chemical dispersion. The study assesses drone operations at heights of 1m, 1.5m, and 2m above hollow cone nozzles in 2.8m/s wind conditions. It aims to evaluate droplet uniformity and dispersion on water-sensitive paper placed on paddy plants, analyzed with ImageJ software. Results show that at 1.5m height, there's a significantly higher average droplet density, with 162.7 deposits/cm² in the upper region and 161.8 deposits/cm² in the lower region. Additionally, coverage is notably increased, at 55.21% for the upper region and 51.4% for the lower region. This research highlights the importance of optimal drone altitude for efficient chemical application in rice fields, improving crop protection and yield.

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A marine-cleaning robot competition was featured at the junior division of the Eighth Underwater Robot Festival held at Iwakuni, Japan in 2022. The theme of the competition was “Life Below Water,” which is the 14th goal of SDGs, and consisted of poster presentations on marine-debris problems and a robot competition to compete in debris-collection ability. In addition, questionnaires were sent out before and after the competition to analyze the level of understanding of robot development and SDGs after the competition. This paper reports an outline of the junior division and results of the educational effect using questionnaire analysis.

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Autonomous Underwater Vehicles (AUVs) require long-distance communication, especially in the deep sea. Acoustics communication and wireless optical communication have limitations of low latency and water conditions effects respectively. Radiofrequency (RF) communication provides a high data rate with free orientation of the transmitter/receiver antennas. However, electromagnetic waves are highly restricted by high attenuation over short distances in the underwater medium. In this study, we investigated the RF communication in a tank full of seawater. A loop antenna and rectangular antenna were used for the base station and AUV respectively. The experiments were conducted to measure the transmission rate with different distances between the base station and the AUV using both User Datagram Protocol (UDP) and Transmission Control Protocol (TCP). Live video streaming with framerate analysis was considered. The results show the effect of the distance between the transmitter and receiver on the transmission rate, in addition, to the antenna's stability has huge effects on the connection stability. We successfully achieved a High-Definition (HD) video streaming with 25fps for over 1 meter.

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The COVID-19 pandemic outbreak transformed the paradigm of the educational process. Many educational institutions, including the University, mainly the Faculty of Computer Science, Brawijaya University, have experienced success with learning through the Internet. The continuance intention factor for using an online learning management system is important to ensure learning sustainability. This study investigates continuance intention factor by using self determination model as a research model. The objective of this study is to find the factor factors affecting students' continuance intention toward online learning management systems. The proposed model is useful for investigating continuance intention factors. The study finds that intrinsic regulation, external regulation, identified regulation, and introjected regulation significantly positive impact on continuance intention in learning management systems. Finally, this study provides suggestions for the Faculty to improve the continuance intention of the student in using an online learning management system.

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In the 21st century, the Internet has undergone unprecedented growth in technology and accessibility, contributing to the achievement of various Sustainable Development Goals (SDGs) related to digital inclusion and technological advancement. This era witnessed the ubiquitous presence of personal computers in daily life, enabling people to access vital information online, thus promoting digital literacy and access to information (SDG 4, SDG 9, SDG 16). Moreover, the rapid expansion of mobile technology has marked a significant milestone in the development of mobile devices, aligning with SDG 9, which emphasizes innovation and infrastructure development. Android and iOS platforms currently dominate the mobile landscape, offering a range of opportunities and challenges for developers. However, the prevalent practice of developing applications exclusively for a single platform has resulted in suboptimal development efficiency, hindering progress toward SDG 9 and sustainability in technological advancements (ESG). In response, developers have begun to explore cross-platform technologies, which not only streamline development across different platforms but also contribute to cost-efficiency and resource optimization (SDG 12). This paradigm shift in development approaches presents a unique opportunity to meet the growing demand for real-time, precise, and diverse weather information. In response, this study proposes the creation of a mobile application-based weather forecast system. This system places a strong emphasis on user interface (UI) design and the successful implementation of key functionalities, promoting user-friendly access to weather data and aligning with the principles of inclusive design and technological accessibility (SDG 4). In summary, this research endeavors to harness the advancements in mobile technology and crossplatform development to deliver an innovative weather forecast system, ultimately contributing to the broader goals of digital inclusion, technological sustainability, and user-centric design in line with SDGs and ESG principles.

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