The Department of Electronics and Information Engineering, Faculty of Science and Engineering, Tokushima Bunri University, offers a combination of lectures, exercises, and experiments in related subjects. This helps students to acquire expertise in computer hardware and software, as well as the ability to learn continuously and use their initiative in taking actions. At the end of each semester, questionnaires are conducted for all subjects to assess students’ level of understanding and the appropriateness of teaching methods. Instructors use this information to assess the status of each subject and strive to improve students’ understanding. In addition, to understand the trends and characteristics of the improvement in students’ abilities and understanding, this study investigates the situation at the end of the second year of study. A questionnaire survey is conducted to ascertain the status of improvement in knowledge and abilities. Based on the obtained response data, the polychoric correlation coefficient is calculated and the data are analyzed using multiple correspondence analysis.

Inclusive design requires empathy for users with diverse needs, yet designers often find it difficult to consider perspectives and needs they have not personally experienced. This study investigated whether simulating left-handedness through human augmentation could enhance design awareness and reduce unconscious bias. The “left-handed experience device,” a wearable robot that simulates left-handedness by mirroring right-handed movements, was developed. Engineering students were divided into three groups: (1) no information about left-handedness, (2) explanation of left-handedness, and (3) experiential learning using the device. Each group conducted product redesign tasks focusing on usability and inclusivity using everyday tools such as calipers and kitchen tools. Design outcomes were evaluated using a self-assessment rubric. The group that used the device (Group 3) demonstrated higher awareness and generated more inclusive and symmetrical designs. Participants also reported increased confidence and empathy toward minority users. These results suggest that human augmentation can serve as a valuable educational tool for fostering inclusive design thinking. While experiential learning was effective in raising awareness, sustainable behavioral change remains a challenge.

This study proposes a robot that can crawl through the bottom of tanks and measure the thickness of its floor plate using an ultrasonic sensor. To prevent the robot from toppling because of the fluid resistance of the oil and to direct the transducer of the ultrasonic sensor perpendicular to the floor plate, a design inspired by a roly-poly toy was implemented. This ensured that the robot remained upright owing to the separation of its center of gravity and center of buoyancy. However, although this structure makes the robot resistant to toppling, it makes it susceptible to tilting owing to fluid resistance. Therefore, a simulator was developed, as part of this project, to estimate the robot’s posture in liquid. Finally, experiments were conducted in an indoor water pool under the assumption of the oil tank to compare the postures estimated by the simulator and actual posture.

This study examines the educational effects of a generative AI music creation workshop conducted as part of STEAM education that integrates the arts (in this case, music creation) into science, technology, engineering, and mathematics (STEM). The workshop, held at Osaka Metropolitan University College of Technology, employed state-of-the-art generative AI tools such as ChatGPT and Suno, a music generation AI. It provided students with the opportunity to write their lyrics and collaborate with AI to compose music. Additionally, AI literacy education was incorporated to deepen understanding of the risks and ethical challenges associated with AI, aiming to balance creative activities with the responsible use of technology. Results from a post-workshop survey indicated that students experienced heightened creativity and self-expression through collaboration with generative AI, and many reported feeling a sense of excitement and anticipation (“wakuwaku”) during the process. Notably, creating music with AI was found to enhance students’ creative self-efficacy and promote sustained motivation for long-term AI use. However, some participants also expressed concerns about over-reliance on AI and reported psychological barriers, highlighting areas for improvement in future implementations. This study not only demonstrates that STEAM education incorporating generative AI is an effective approach to fostering students’ creativity and motivation to learn, but also addresses practical challenges such as ethical considerations and the need for supportive educational environments. Furthermore, as a successful case supported by The Asia Foundation and Microsoft, the program suggests the potential for collaboration among industry, academia, and government in advancing STEAM education.

Beach pollution, particularly the accumulation of garbage on seashores in Japan, poses a significant environmental threat. To tackle this persistent issue, beach cleaning activities are necessary. However, beach cleaning is not always scheduled regularly. It depends on the availability of volunteers, resulting in situations where they struggle to clean the beach due to excessive garbage or where there is no garbage left to clean, as the beach was cleaned a short while earlier. Hence, this paper proposes an unmanned aerial vehicle drone-based monitoring system powered with a garbage detection deep learning model. This system also integrates drone technology with a beach-cleaning ground robot to create an efficient cleanup system by planning the route for garbage collection. The research focuses on detecting garbage, locating garbage in the real world, clustering detected locations, and planning paths for ground robots. The system utilizes real-time garbage detection with the YOLOv8 model and georeferencing techniques to map garbage locations accurately. The project also employs hierarchical density-based spatial clustering of application with noise (HDBSCAN) and simulated annealing for optimal route planning. Experiments conducted in simulated and real-world environments, including Hokuto no Mizukumi Seaside Park and the Kyushu Institute of Technology Sports Ground, assessed the system’s accuracy. The results revealed a high detection accuracy of 98.33% in simulations, with an average root mean square error (RMSE) offset error of 0.25 m. In contrast, real-world conditions presented more challenges, resulting in a lower accuracy of 84.26% and an average RMSE of 1.96 m.

Global and local localizations are critical for mobile robots to perform tasks without human support. In this study, we proposed a new method for global and local localizations that combines an environmental magnetic field with a scan matching method. In this method, we record the environmental magnetic field at each waypoint during the map-building process. To perform global localization, we rotate the mobile robot’s magnetic sensor-detected heading direction (current magnetic heading direction) to approximate the heading direction of the environmental magnetic field stored on the map. Subsequently, we use the scan matching method at positions around each waypoint. The mobile robot is currently in the position with the highest matching rate. The experimental results show that the mobile robot can successfully localize almost anywhere in the indoor environment, with the exception of a position where the magnetic sensor is affected by electric boards, and has a high success rate in an outdoor environment. For the local localization, we calculate the deviation between the mobile robot’s current magnetic heading direction and the one stored on the map, and then use this deviation to improve the localization using the scan matching method. The experimental results show that the mobile robot can perform much stronger localization during autonomous navigation.

In congested environments, achieving safe and efficient robot navigation without disturbing crowd flow is a significant challenge. Previous studies have not sufficiently validated motion control algorithms that account for the dynamic characteristics of pedestrian flows, leading to degraded navigation performance. In this paper, a merging and following control algorithm based on the real-time understanding of pedestrian flow dynamics is proposed to address this issue. The advanced recognition of pedestrian flows is enabled by the use of an overhead-view RGB camera and 2D-LiDAR, through which crowd dynamics are accurately captured. Based on the acquired dynamic flow information, merging motions that preserve crowd movement and following motions adapted to nonuniform flows are generated. The proposed system is evaluated in both nonuniform flow and separate flow scenes, which represent general crowd behavior. Multiple simulations and real-world experiments have demonstrated that the proposed system enhances navigation performance compared with conventional systems while ensuring human safety and comfort. In particular, reductions in travel time and path length, decreases in average acceleration and collision count, and the minimization of social force on pedestrians are achieved. These validation results indicate that the system enables robots to navigate crowds in a human-like manner, thereby supporting its potential deployment in practical environments.

Pneumatic artificial muscles (PAMs) are soft actuators that generate tension via contraction when supplied with compressed air. Although PAMs have been widely used in robots mimicking endoskeletal organisms, recent advancements in millimeter-scale thin PAMs have enabled a more precise replication of complex musculoskeletal systems. In contrast, exoskeletal organisms, such as crustaceans and insects actuate their joints using pennate muscles embedded within their exoskeletons. However, integrating actuators into the exoskeletons of exoskeletal-inspired robots is challenging owing to spatial constraints. Consequently, wire and servo-driven mechanisms are predominantly employed, and studies on exoskeletal robots incorporating the muscle-apodeme structures of exoskeletal organisms remain largely unexplored. To address this gap, this paper presents the development of a crab-inspired robotic walking leg featuring an exoskeletal structure with embedded pneumatic artificial pennate muscles, modeled after the muscle-apodeme structures of snow crabs. The experimental evaluations demonstrated that the robot successfully performed joint opening and closing motions, achieving a range of motion comparable to that of a snow crab.

This paper presents a fully onboard control and planning system for indoor quadrotor navigation, leveraging modular control and lightweight motion planning. Autonomous operation of unmanned aerial vehicles (UAVs) in indoor environments, such as inspection, monitoring, and mapping, faces challenges due to the absence of global navigation satellite system (GNSS) and limited onboard computational resources. Existing methods often rely on external localization systems or high-specification CPUs, restricting deployment on compact, low-power UAVs. To address this, we develop a hierarchical control architecture comprising motor, attitude, velocity, and position controllers, all implemented with full-state feedback. Paired with a custom lightweight motion planning algorithm, the system operates efficiently on resource-constrained CPUs such as the Raspberry Pi. This approach enables affordable, low-power UAVs to achieve autonomous indoor navigation without external infrastructure. The results show stable flight performance and computational efficiency, validating suitability for GNSS-denied environments.

Similar to the demand for automated driving vehicles and autonomous mobile robots on land, marine autonomous surface ships are desired in the maritime field. An autonomous ship must be capable of recognizing objects in its vicinity and accurately determining the relative positions between the ship and objects. In this paper, we propose a novel method for creating a map of the ship’s surroundings using trinocular stereo vision, along with accurately estimating its location and motion within this map. In the proposed method, the world coordinates of feature points—such as land-based structures—are measured at the ship’s initial position. As the ship moved, these feature points in the images are tracked using optical flow. The ship’s world coordinates are then estimated based on the pixel and world coordinates of these feature points. Furthermore, the feature points in the three-dimensional map are classified using deep learning. An experiment involving ship navigation over a distance of 1000 m confirmed that the system can accurately estimate the position, heading, and speed of the ship and can also identify quay walls, other ships, and bridges in three-dimensions. The results indicate that the proposed method is suitable for automating berthing systems.