Various meal-assistance robot (MAR) systems are being studied, and several products have already been commercialized to alleviate the imbalance between the rising demand and diminishing supply of meal care services. However, several challenges remain. First, most of these services can serve limited types of western food using a predefined route. Additionally, their spoon or fork sometimes makes it difficult to acquire Asian food that is easy to handle with chopsticks. In addition, their limited user interface, requiring physical contact, makes it difficult for people with severe disabilities to use MARs alone. This paper proposes an MAR system that is suitable for the diet of Asians who use chopsticks. This system uses Mask R-CNN to recognize the food area on the plate and estimates the acquisition points for each side dish. The points become target points for robot motion planning. Depending on which food the user selects, the robot uses chopsticks or a spoon to obtain the food. In addition, a non-contact user interface based on face recognition was developed for users with difficulty physically manipulating the interface. This interface can be operated on the user’s Android OS tablet without the need for a separate dedicated display. A series of experiments verified the proposed system’s effectiveness and feasibility.

The market for electric vehicles is growing rapidly. Among them, the demand for a dual motor type 4 WD (Four -Wheel Driving) system is increasing. In this paper, we present the Torque Matching Strategy (TMS) method to select the optimal torque distribution ratio for dual motors. The TMS controller operates to set the optimal efficiency point by linearizing the drive efficiency combination of the two motors. Driving simulation and testing were performed through five drive cycles in the driver model interworking environment implemented in MATLAB and Carsim. The optimal distribution ratio was derived according to the front and rear gear ratios under the load condition, and the driving was verified by comparing it with the TMS control method. The efficiency was numerically verified by comparing the power loss of the driving motor. It reduced up to 34% in Urban Dynamometer Driving Schedule and up to 56.3% in Highway fuel efficiency test. The effectiveness of the TMS control method was demonstrated through the distribution rate trend based on the operation cycle and power loss.