Home Robot Control System Based on Internet of Things and Fuzzy Control

The rapid development of intelligent control technology has improved the functions of service robots oriented to the home environment, and the functional requirements of family members for service robots have also been upgraded from simply liberating hands and reducing housework to emotional communication and intelligent escort. Based on the Internet of Things and fuzzy control technology, this paper builds a home robot control system and gives a brief overview around the mechanical structure design of the home service robot, mainly focusing on the core control system and global path planning methods. Moreover, this paper adopts the control system structure that combines the upper computer and the bottom motion controller and combines it with simple and practical system software, so the system stability is high. Finally, this paper verifies the performance of the system constructed in this paper through experimental research. The research results show that the system constructed in this paper has certain practical effects.

Optimal Waiting Position of a Home Robot for Risk Communication Considering Behavior Patterns of an Occupant

The risk communication in a home between a home robot and an occupant must be smooth in a way that the home robot does not disturb the occupant lives. In this paper, we propose a new method to determine the optimal waiting position considering the personal space and the obstacles such as furniture and the occupant’s walking patterns. It is shown that the distance to the wall from the occupant in the direction of the home robot and the standing or sitting posture affect most on the personal space. Furthermore, this personal space is dependent on each individual preference. The performance of the proposed method is much more feasible compared with those obtained in our previous approach.

Extraction of Parameters for 90-degree Turn Prediction Using the IMU-based Motion Capture System

Against the increasing number of single households, we have been proposing the “Biofied Building” that provides a safe, secure, and comfortable living space for a resident using a small home robot. The robot can be used for real-time sensing of the resident’s position and behavior. On the other hand, for further use of the robot, such as choosing a path that does not disturb the resident, a phase to predict the resident’s behavior is necessary. Walking, which is one of the most basic activities of daily living, is often targeted in studies of motion prediction. However, most of them deal with steady walking, even though walking in daily life includes unsteady walking such as the turning motion. Therefore, the purpose of this study was to extract the prediction parameters to construct a prediction method for the unsteady 90-degree turn. In this study, we explored the effective prediction parameters for 90-degree turns based on the measured data using the inertial measurement unit (IMU) based motion capture system aiming to introduce the prediction of unsteady walking to the “Biofied Building”.

Designing Personas for Expressive Robots

Imbuing robots with personality has been shown to be an effective design approach in HRI, promoting user trust and acceptance. We explore personality design in a non-anthropomorphic voice-assisted home robot. Our design approach developed three distinct robot personas: Butler, Buddy, and Sidekick, intended to differ in proactivity and emotional impact. Persona differences were signaled to users by a combination of humanoid (speech, intonation), and indirect cues (colors and movement). We use Big Five personality theory to evaluate perceived differences between personas in an exploratory Wizard of Oz study. Participants were largely able to recognize underlying personality traits expressed through these cue combinations in ways that were consistent with our design goals. The proactive Buddy persona was judged as more Extravert than the more passive Sidekick persona, and the Butler persona was perceived as more Conscientious and less Neurotic than either Buddy or Butler personas. Users also had clear preferences between different personas; they wanted robots that mimicked but accentuated their own personality. Results suggest that future designs might exploit abstract cues to signal personality traits.

Evaluation of the Established IoT Smart Home Robot Teaching Module Based on Embedded Thematic-Approach Strategy

Artificial intelligence (AI) technology–based intelligent robots are constructed using different technologies, such as Internet of Things (IoT), big data, deep learning, machine learning, neural network, and expert system. This particular type of robots can increase the work efficiency of humans and improve their quality of life. From the industry perspective, AI robots possess unlimited potential for development, and they are projected to be a 10-trillion-dollar industry. In this study, the critical technology of IoT is applied to develop a teaching module for an IoT smart home robot. Teaching and evaluation are performed through an embedded thematic-approach teaching strategy in the course named Automatic Measurement and Monitoring. This research aims to teach students how to integrate IoT technology into robot design and construction to build IoT smart home robots. This cross-disciplinary research incorporates emerging technology—integration of smart home, robot construction, and IoT technologies—into industrial education, teaching material and equipment development, and experimental teaching and evaluation. The participating students were juniors or seniors from the Department of Electrical Engineering or Electromechanical Engineering at the University of Technology.

Assessing the Feasibility of Augmenting Fall Detection Systems by Relying on UWB-Based Position Tracking and a Home Robot

Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand.