# Remote Control of a Mobile Robot for Indoor Patrol
## 1 Introduction  
Over the past few years, different types of wheeled mobile robots (WMR) have been proposed. The WMR's advantages include high mobility, high load, and easy control. Previous WMR studies have chiefly focused on developing effective performance and helping people work in various environments. Many robots have been used in different applications such as indoor services, space exploration, military undertakings, entertainment, healthcare services, _etc._ In recent years, intelligent control has been applied to WMR fora variety of tasks [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. One of the most common intelligent controllers is the fuzzy controller [10; 11; 12; 13] because the design is relatively simple and flexible. This paper presents three control schemes. The first one is the smartphone remote control, which uses the Zigbee wireless transmission to the computer-controlled robot. Forward, backward, left-turn, and right-turn movements can be performed via the smartphone. The second control scheme utilizes the EYECAM camera and image processing to detect moving objects and calculate the center distance, and then applies fuzzy controller to track the moving object. The moving target can be clearly seen on the screen of the smartphone. In the third scheme, the WMR integrates infrared sensors to the fuzzy control system and distance detection. A camera is put on the WMR for surveillance. The surroundings of the robot can be seen on the phone. Home patrol and monitoring can therefore be accomplished. With advanced technology and industrial development, path following, navigation, target tracking, multi-vehicle coordination, path planning and other applications of WMR have been widely discussed. Many of the applications use intelligent systems in controller design such as fuzzy system and neural network. Fuzzy system is quite similar to human thinking. In the real world, the fuzzy logic is full of greater fault tolerance, has better promotion, and is better suited for a nonlinear system. When the WMR is meant to work in a variety of environments, the choice of the controller becomes a very important issue. In the paper of [PERSON] _et al._[10], the automobile used a visual sensor to track a moving object. The authors utilized a simplified type 2 fuzzy system to a WMR for target-following control. [PERSON] _et al._[12] used a rapid path-planning and fuzzy logic control to make the WMR trace expected paths. [PERSON] _et al._[14] proposed a method that is based on the integration of a focused Dsearch algorithm and dynamic window local obstacle-avoidance algorithm with some adaptations that provide efficient avoidance of moving obstacles. In the paper of [PERSON] _et al._[PERSON] _et al._ (2015), a position-control algorithm was proposed with two separate feedback loops: a velocity feedback loop and a position feedback loop. The proposed control algorithm is designed to compensate for both internal and external errors. This algorithm makes it possible for the WMR to accurately follow the designed trajectory. Other studies include the use of image processing and a cerebellar model articulation controller for path-following control [1], in addition to the laser sensor-positioning algorithm in controller design for unknown environment map building [4], automobile ultrasonic sensors [6], a localization system for path planning [7], and distance between the object and the camera [9]. In this study, remote control by smartphone in mini vehicle applications [16; 17] is proposed. In our previous work [17], the mobile robot can only be turned on via Wi-Fi, and performed preset path-following control. In this study, more functions are added to the mobile robot. In addition to the turn-on control, dynamic object tracking and obstacle-avoidance controls are added. The robot can also be controlled by a personal computer (PC) or a smartphone remotely, and the moving path can be changed by the operator at the remote site.