Most cleaning robots have a good cleaning performance for small environments such as houses but require a longer cleaning time due to problems such as slow cleaning progress and low battery capacity, making the robots unsuitable for large environments such as libraries and airports. Cleaning large environments with multiple robots is faster than cleaning them with a single robot. Multi-cleaning robots can utilize several robots to simultaneously clean and share the task of cleaning among the robots. However, as the number of robots increases and the effective distribution of cleaning is not efficient during the cleaning process, the cleaning time will consequently be longer due to the frequent collisions between the robots. Therefore, to shorten the cleaning time, multi-cleaning robots require a coverage path planning that uses an effective cleaning distribution method in the cleaning process. In this paper, a coverage path planning using a cleaning distribution method based on map decomposition is proposed to reduce the cleaning time of multi-cleaning robots. The experimental results demonstrated that the proposed multi-cleaning robots' coverage path planning could be used in large environments in the presence of several types of obstacles. Furthermore, the cleaning time was found to be shorter than that of the previous methods in the case of multi-cleaning robots. INDEX TERMS Coverage path planning, cleaning robot, computational efficiency, multi-robots system, robot path planning.

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This paper proposes a system for estimating the level of danger when a driver accesses the center console of a vehicle while driving. The proposed system uses a driver monitoring platform to measure the distance between the driver’s hand and the center console during driving, as well as the time taken for the driver to access the center console. Three infrared sensors on the center console are used to detect the movement of the driver’s hand. These sensors are installed in three locations: the air conditioner or heater (temperature control) button, wind direction control button, and wind intensity control button. A driver’s danger level is estimated to be based on a linear regression analysis of the distance and time of movement between the driver’s hand and the center console, as measured in the proposed scenarios. In the experimental results of the proposed scenarios, the root mean square error of driver H using distance and time of movement between the driver’s hand and the center console is 0.0043, which indicates the best estimation of a driver’s danger level.

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