In this study, an Artificial Intelligence of Things (AIoT)-based automated picking system was proposed for the development of an online shop and the services for automated shipping systems. Speed and convenience are two key points in Industry 4.0 and Society 5.0. In the context of online shopping, speed and convenience can be provided by integrating e-commerce platforms with AIoT systems and robots that are following consumers’ needs. Therefore, this proposed system diverts consumers who are moved by AIoT, while robotic manipulators replace human tasks to pick. To prove this idea, we implemented a modified YOLO (You Only Look Once) algorithm as a detection and localization tool for items purchased by consumers. At the same time, the modified YOLOv2 with data-driven mode was used for the process of taking goods from unstructured shop shelves. Our system performance is proven by experiments to meet the expectations in evaluating efficiency, speed, and convenience of the system in Society 5.0’s context.

To achieve Industry 4.0 solutions for the networking of mechatronic components in production plants, the use of Internet of Things (IoT) technology is the optimal way for goods transportation in the cyber-physical system (CPS). As a result, automated guided vehicles (AGVs) are networked to all other participants in the production system to accept and execute transport jobs. Accurately tracking the planned paths of AGVs is therefore essential. The omnidirectional mobile vehicle has shown its excellent characteristics in crowded environments and narrow aisle spaces. However, the slip problem of the omnidirectional mobile vehicle is more serious than that of the general wheeled mobile vehicle. This paper proposes a slip estimation and compensation control method for an omnidirectional Mecanum-wheeled automated guided vehicle (OMWAGV) and implements a control system. Based on the slip estimation and compensation control of the general wheeled mobile platform, a Microchip dsPIC30F6010A microcontroller-based system uses an MPU-9250 multi-axis accelerometer sensor to derive the longitudinal speed, transverse speed, and steering angle of the omnidirectional wheel platform. These data are then compared with those from the motor encoders. A linear regression with a recursive least squares (RLS) method is utilized to estimate real-time slip ratio variations of four driving wheels and conduct the corresponding compensation and control. As a result, the driving speeds of the four omnidirectional wheels are dynamically adjusted so that the OMWAGV can accurately follow the predetermined motion trajectory. The experimental results of diagonally moving and cross-walking motions without and with slip estimation and compensation control showed that, without calculating the errors occurred during travel, the distances between the original starting position to the stopping position are dramatically reduced from 1.52 m to 0.03 m and from 1.56 m to 0.03 m, respectively. The higher tracking accuracy of the proposed method verifies its effectiveness and validness.

This paper plans to establish a warehouse management system based on an unmanned aerial vehicle (UAV) to scan the QR codes printed on packages. This UAV consists of a positive cross quadcopter drone and a variety of sensors and components, such as flight controllers, single-board computers, optical flow sensors, ultrasonic sensors and cameras, etc. The UAV stabilizes itself by proportional-integral-derivative (PID) control and takes pictures of the package as it reaches ahead of the shelf. Through convolutional neural networks (CNNs), the placement angle of the package can be accurately identified. Some optimization functions are applied to compare system performance. When the angle is 90°, that is, the package is placed normally and correctly, the QR code will be read directly. Otherwise, image processing techniques that include Sobel edge computing, minimum circumscribed rectangle, perspective transformation, and image enhancement is required to assist in reading the QR code. The experimental results showed that the proposed algorithm provided good performance of a recognition rate of 94% for the stochastic gradient descent (SGD) and 95% for Adadelta optimization functions. After that, successful QR code reading was presented.

According to the data from Alzheimer’s Disease International (ADI) in 2018, it is estimated that 10 million new dementia patients will be added worldwide, and the global dementia population is estimated to be 50 million. Due to a decline in the birth rate and the development and great progress of medical technology, the proportion of elderly people has risen annually in Taiwan. In fact, Taiwan has become one of the fastest-growing aged countries in the world. Consequently, problems related to aging societies will emerge. Dementia is one of most prevailing aging-related diseases, with a great influence on daily life and a great economic burden. Dementia is not a single disease, but a combination of symptoms. There is currently no medicine that can cure dementia. Finding preventive measures for dementia has become a public concern. Older people should actively increase brain-protective factors and reduce risk factors in their lives to reduce the risk of dementia and even prevent the occurrence of dementia. Studies have shown that engaging in mental or creative activities that stimulate brain function has a relative risk reduction of nearly 50%. Elderly people should develop the habit of life-long learning to strengthen effective neural bonds between brain cells and preserve brain cognitive functions. Playing chess is one of the suggested activities. This paper aimed to develop a Chinese robotic chess system for the elderly. It mainly uses a camera to capture the contour of the Chinese chessman, recognizes the character and location of the chessman, and then transmits this information to the robotic arm, which will grab and place the chessman in the appropriate position on the chessboard. The camera image is transmitted to MATLAB for image recognition. The character of the chessman is recognized by convolutional neural networks (CNNs). Forward and inverse kinematics are used to manipulate the robotic arm. Even if the chessmen are arbitrarily placed, the experiment showed that their coordinates can be found through the camera as long as they are located within the working scope of the camera and the robotic arm. For black chessmen, no matter how many degrees they are rotated, they can be recognized correctly, while the red ones can be recognized 100% of the time within 90° of rotation and 98.7% with more than a 90° rotation.