Enhanced Behavior System of Service Robots based on BERT, CNN and DQN

Abstract

Purpose In the past 20 years, the research in the field of robotics and intelligent systems has gradually increased. With the advancement of related fields, service robots have received extensive attention from industry and academia (R. Reddy, 2006). The increasingly serious aging trend has brought great challenges to the current society. Due to illness or physical decline, it is difficult for the elderly to carry out their daily life smoothly. Service robots can actively interact with the elderly and provide corresponding help. For example, reminding meals, reminding taking medicines, assisting in doing sports and so on are possible (Mast M et al., 2015). For the safety assurance of the elderly, the robot should be actuated properly for speed regulation and strong recognition ability, combined with an audiovisual system to prevent accidental collision with elderly people. In elderly care, robots need to respond to the voice commands of the elderly, and current speech recognition technology makes this possible. (Park C et al., 2012). This paper proposes a well-established software engineering approach for properly coordinating service robotic systems. Method The core of the service robot behavior system is the interaction and cooperation between the elderly and the service robot, which is important to design a simple and stable system. Therefore, when building a service robot behavior system, it is necessary to clearly define the system architecture to complete the corresponding behaviors. Based on the three artificial intelligence algorithms of BERT, CNN, and DQN, this paper proposes a service robot platform to perform corresponding behaviors according to categories by analyzing the spoken instructions of speakers. (Figure 1). Through an audiovisual system to obtain the activity information of the elderly, the safety status and cognitive status of the elderly are monitored. Service robots collect data through cameras and microphones and then pass it on to other systems. The main purpose of a behavior system is to ensure safety and reduce the burden on caregivers by tracking the behavior of the elderly and providing the caregivers with the latest status reports. In this system, the BERT algorithm takes the processed sound data as input, outputs relevant features, and classifies them through CNN. Pre-defined event categories closely related to the daily life of the elderly are obtained. The actuator system provides the locomotion capabilities of service robots. DQN performs fixed behaviors through camera data and event categories to help the elderly go about their daily lives. (Figure 2). Results and Discussion A service robot should be able to perform several behaviors autonomously to provide various services for elderly beings in a dynamic and partially unknown environment by applying both technology and knowledge. The service robot proposed in this paper accurately is divided into the audiovisual, behavior, and actuator systems, which enables expandable scalability and module-based maintenance. Specially, by utilizing BERT and CNN, the performance of recognizing voices is improved more than traditional approaches. In the future, it is required to enhance data transfer structures among the systems.