# Navigating a Service Robot for Indoor Complex Environments
## 1 Introduction  
Today's workers in care-taking facilities have their hands full taking care of patients, thus require help for everyday routines. There are many branches of investigation into using machines to help out with daily routines in the health-care industry, such as gesture recognition [1], or pedestrian movement prediction [2]. We choose to investigation helpful tasks, such as delivering proper medicines to target patients, or helping to carry heavy loads in follow-me mode, performed by an autonomous robot with face and gender recognition abilities. For these purposes, this paper presents a tri-wheeled autonomous service robot equipped with a camera, an RGB-Depth (RGB is acronym for Red, Green, and Blue) sensor, and sonar sensors. It has built-in facial recognition ability, ability to separate staff or patients from visitors, and it can also distinguish the gender of a visitor for record-keeping purposes. By recognizing staff, it can offer the staff functions not available to patients or visitors, such as the follow-me function. By recognizing patients, it can help dispense the proper medication to each individual patient. A possible scenario for this robot could be using it to dispense medicine to selected patients who may be walking around the hallways. In navigation, it can avoid static as well as dynamicobstacles while moving toward its objective, using a dual-level Adaptive Neuro-Fuzzy Inference System (ANFIS)-based fuzzy controller. It uses a depth-map to detect obstacles ahead, then uses image processing techniques to extract information as input into an ANFIS-based fuzzy system for analysis in order for the service to be able to avoid obstacles. The robot also incorporates sonar information using another ANFIS-based fuzzy system when it determines that depth-map information is insufficient. In 1998, [PERSON] and [PERSON] [3] proposed an idea to include extra distance sensors on robots, such as laser distance sensors and ultrasound distance sensors. The distance sensors can aid in detecting surrounding objects or obstacles in real time so that the robot can locate or correct itself using known map data. However, the high costs of the precision sensors make them unsuitable for popular use. Even though ultrasonic sensors are relatively cheap by comparison, their deviation of errors is wider and they have detection blind spots; the detection distance is only proportional to the volume of the sound generator. [PERSON] [4] proposed a driver-less car that has face detection and tracking capabilities. Similarly, we added onto our architecture the ability to perform face and gender recognition. [PERSON] [5] proposed the development of a sensing system in an indoor environment, allowing the robot to have autonomous navigation and the ability for identification of its environment. His proposed system consisted of two parts: The first part is a reactive navigation system where the robot uses the RGB-Depth sensor to receive depth information and uses the arrangement of obstacles as the basis to determine the path to avoid the obstacles indoors; the second part uses an artificial neural network to identify different configurations of the environment. [PERSON] [6] introduced an improved version based on fuzzy rules. His system uses 16 rules, three inputs and one output, one Mamdani-type fuzzy controller, and obtained acceptable results in real-time experiments. In 2016, [PERSON] [7] presented the possibility of a fuzzy control system, with the output from depth sensor as its input, for an autonomous wheelchair as a possible design. In 2018, [PERSON] [8] and [PERSON] et al. [9] show that fusion of data from multiple sensors can perform better than a single sensor for a driver-less vehicle. Our study builds an autonomous robot with a camera, a RGB-depth sensor, and sonar sensors. Prior to moving, the robot scans the entire room with a pre-trained pedestrian classifier in order to determine if a targetable person or persons exist that need to be tracked.