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.

This paper presents a novel fire detection device in the highly complicated urban environment, such as an office building, supermarket, school, etc. A robot for fire detecting and/or search and rescue needs to move fast while searching wide area. For this, we had developed an thermal image camera module which is capable of selectively acquiring very large field of view (FOV) - panoramic view - images or narrow field of view images from a single viewpoint, at video rates. An image processing and device controlling algorithm to detect a fire fastly and accurately was developed also. The developed device was tested in the indoor environments and the feasibility of the device was evaluated. We expect that the application of the proposed device is not limited to fire fighting. Its application will include a military, security and scientific field. For the further works, we will improve the performance of the detection algorithm in the proposed device and test it using a mobile platform in a realistic environment.

Wheeled mobile robots have a significant role in the mobility industry. When outdoor mobile robots perform given tasks in urban environments, they often face the challenge of climbing and descending curbs. Many commercial mobile robots rely on ramps to navigate curbs. However, for outdoor mobile robots to achieve human-like freedom of movement in urban spaces, they must be capable of climbing and descending curbs. A six-wheeled mobile robot can be a solution to this problem since it can effortlessly climb or descend curbs. When driving on flat terrain, however, driving with six wheels is quite inefficient, wasting energy, and driving with four wheels would be better than using six wheels. Therefore, when operating a six-wheeled mobile robot, we need to determine the appropriate mode to use, specifically the six-wheel mode or the four-wheel mode, depending on the type of the road. In this paper, we present a method that determines the appropriate mode using the video captured from the frontal camera. We employ 3D CNN and the Bayesian fusion to determine the optimal mode for the six-wheeled robot and optimal timing for mode change. We collect a set of video sequences and test our method on these collected video sequences.