Given the prospect of low birth rate and aging population among developed countries, which further resulted in the shortage of workforces, service robots have been gradually applied to real-life from past academic research. This paper introduces the development of a service robot that is designed for food service in fast-food restaurants with the innovative improvement of mapping, localization, and navigation. Moreover, this research took the initiative of integrating 3D point cloud map and 2D occupancy grid map (OGM) in order to build a PC-OGM. In another word, using the sensory fusion method allows the service robot to adapt to a more complicated environment as well as enhance its positioning. In terms of its navigation function, the adaptive motion controller is refined so the service robot could navigate through narrow aisles smoothly. Finally, friendly contact-free food service robots were evaluated at fast-food restaurants in order to gain feedback from diners and waiters. Their feedback was broken down into 3 categories, availability, reliability, and satisfaction, for further analysis. As COVID-19 assails the world, we also look into future possibilities of food service robot deployment among restaurants to keep food and surface free from the virus.

In the past few years, due to the growth of the open-source community and the popularity of perceptual computing resources, the ROS (Robotic Operating System)Ecosystem has been widely shared and used in academia, industrial applications, and service fields. With the advantages of reusability of algorithms and system modularity, service robot applications are flourishing via the released ROS navigation framework. In the ROS navigation framework, the grid cost maps are majorly designed for path planning and obstacle avoidance with range sensors. However, the robot will often collide with dynamic obstacles since the velocity information is not considered within the navigation framework in time. This study aims to improve the feasibility of high-speed dynamic obstacle avoidance for an ROS-based mobile robot. In order to enable the robot to detect and estimate dynamic obstacles from a first-person perspective, vision tracking and a laser ranger with an Extend Kalman Filter (EKF) have been applied. In addition, an innovative velocity obstacle layer with truncated distance is implemented for the path planner to analyze the performances between the simulated and actual avoidance behavior. Finally, via the velocity obstacle layer, as the robot faces the high-speed obstacle, safe navigation can be achieved.

In recent years, multi-robot control systems and service robots equipped with graphical computing have been introduced in various application scenarios. However, the long-term operation of VSLAM calculation leads to reduced energy efficiency of the robot, and accidental localization failure still persists in large-scale fields with dynamic crowds and obstacles. This study proposes an EnergyWise multi-robot system based on ROS that actively determines the activation of VSLAM using real-time fused localization poses by an innovative energy-saving selector algorithm. The service robot is equipped with multiple sensors and utilizes the novel 2-level EKF method and incorporates the UWB global localization mechanism to adapt to complex environments. During the COVID-19 pandemic, three disinfection service robots were deployed to disinfect a large, open, and complex experimental site for 10 days. The results demonstrated that the proposed EnergyWise multi-robot control system successfully achieved a 54% reduction in computing energy consumption during long-term operations while maintaining a localization accuracy of 3 cm.

The challenge in developing an AI deep learning model for motor health diagnosis is hampered by the lack of sufficient and representative datasets, leading to considerable time and resource consumption in research. Therefore, this paper focuses on the analysis of the second harmonic component fault characteristic induced by inter-turn short circuits (ITSCs) in phase voltages. First, it establishes a coil inter-turn short-circuit fault (ITSCF) model of the motor to identify the twice-frequency q-axis voltage error characteristics. Subsequently, it develops simulation programs by integrating control and fault models in MATLAB/Simulink/Simscape to observe and analyze the q-axis voltage and circulating current errors caused by the short circuit. Finally, a discrete wavelet transform method is established to analyze the q-axis synchronous reference frame voltage. By applying the energy-based method to extract the twice-frequency voltage error characteristics, the approach successfully detects the error features and confirms ITSCF in the motor. The contributions of this paper include not only the development of an ITSCF characteristic model for the motor but also the successful application of wavelet transform to effectively analyze the time-frequency characteristics of its signals. This approach can serve as a valuable reference for the design of deep learning models in future AI applications.

Recent years have seen a surge in service robots built on the ROS framework, where effective path planning algorithms are crucial for their successful operation. However, real-world operation presents challenges due to changes in environmental features and unexpected dynamic obstacles. If the path planning algorithms fail to update the state of the map layers in a timely manner, it can result in mission failures for the robots. Therefore, this study proposes a novel Motion Trajectory Analysis Regulator Navigation Framework within the ROS system. This framework incorporates path planning regulators for both global and local path planning algorithms, effectively mitigating path planning failures caused by dynamic environmental changes. For enhanced flexibility in path planning and dynamic obstacle avoidance, this framework proposes the ADRRT* algorithm for global path planning and the FGM-DWA algorithm for local path planning, replacing the traditional A* and DWA algorithms, respectively. Real-field experiments with a service robot demonstrate that the proposed methods not only ensure successful navigation with 100% success rate and a 23% improvement in time efficiency but also effectively handle dynamic and U-shaped obstacles, overcoming the challenge of path planning failures.