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The number of COVID-19 cases in Indonesia has increased significantly of late. Therefore, isolation rooms are needed in hospitals for patient treatment. Room sterilization and disinfection are strictly required as it is mandatory to protect the medical personnel. Chemical and physical methods can be used for sterilization and disinfection. Of these, the ultraviolet C (UVC) light method is the best because it has no residual. Even though UVC light is hazardous for humans' skin and eyes, such hazard can be avoided by eliminating human operators during usage. Thus, we developed a mobile robot with a UVC light system installed at the top and bottom to emit UVC light. We called this robot the Automated UVC Light Mobile Robot (AUMR). The AUMR can be operated automatically as it has a magnetic line sensor and employs a fuzzy inference system algorithm for its movement. The experiment showed that UVC light has good sterilization and disinfection performance in three room types: positive-pressure rooms, negative-pressure rooms, and standard public rooms.

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle's position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

Mapping and navigation on robots are now widely applied in areas such as industry, home appliances, military, exploration and automated vehicles. Mapping and navigation of robots are essential for use in closed environments that are hard to reach by humans. With the mapping and navigation on the robot can allow the robot to recognize the surrounding environment.Data from LIDAR sensors can be converted to maps of the surrounding environment and can be used as an estimate of the position of robots in a closed environment. The distance data from the LIDAR sensor and the LIDAR sensor position status data are converted into Cartesian axes and processed into local maps. Localization using LIDAR sensors are used as a reference to updating global maps.