# Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods
## 5 Conclusions  
In this study, an intelligent control scheme was proposed which can control a robot to perform outdoor patrol tasks day and night, and provide environmental security services. We could successfully integrate the satellite positioning system, path planning, path navigation, laser rangefinder, webcams, fuzzy controller, omnidirectional wheels robot, robotic arm, and convolutional neural network image processing to a wheeled mobile robot. Path guidance used outdoor navigation and positioning system, to obtain location information from the GNSS RTK positioning system. The Dijkstra algorithm and the ant algorithm for shortest path planning were used to compute the desired path. The GNSS RTK positioning system was used to provide the current position of WMR and this system can receive GPS + BDS observation data. As the number of useful satellites has become more than twice the number of a single satellite system, the positioning accuracy has therefore improved significantly. The laser rangefinder and the fuzzy controller were used to detect the surroundings and avoid obstacles in real-time. In image processing, a normal image was in RGB color space, and the produced color was not so intuitive and susceptible to light interference. An object with the light intensity changes will have a large difference in the same image and is difficult to distinguish for identification. Thus, we used HSV color space to solve the problem of light interference, as it can counter the influence of the light source more effectively. It also has a better identification effect for the light source changes during a night patrol. In face recognition, we used a simplified convolutional neural network for classification, and the experimental results prove the feasibility of this method. Two webcams and image processing were used to acquire and recognize the images of the patrol process, detect the intruder who appears on the patrol path, and capture the photo of the intruder. Although the robot sometimes recognizes people as strangers due to light problems during nighttime, as long as the staff of the central control center check the photo of the intruder sent by the robot, they can confirm whether a stranger has invaded. Experiment results show that the WMR can complete outdoor patrol tasks during the day or night, successfully detect the intruder in the patrol process, send the intruder's photo to the control center, and therefore validate the effectiveness of the control scheme that was proposed in this study. The outdoor security robot designed in this study has the advantages of replacing human patrol and reducing manpower requirements, as well as succeeded in cheaper assembly costs of the robot.  
**Author Contributions:** Conceptualization, [PERSON]-[PERSON] and [PERSON]; methodology, [PERSON]-C.C. and [PERSON]-[PERSON]; software, W.-C.C.; validation, [PERSON]-C.C. and [PERSON]-[PERSON]; formal analysis, [PERSON]-C.C. and [PERSON]-[PERSON]; investigation, [PERSON]-C.C. and [PERSON]-[PERSON]; resources, [PERSON]-[PERSON] and [PERSON]-[PERSON]; data curation, W.-C.C.; writing--original draft preparation, W.-C.C.; writing--review and editing, [PERSON]-[PERSON]; visualization, [PERSON]-[PERSON] and [PERSON]; supervision, [PERSON]; project administration, [PERSON]-[PERSON]; funding acquisition, [PERSON]-[PERSON]. Both authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministry of Science and Technology (Taiwan), grant number MOST 106-2221-E-019-002. The APC was funded by National Ocean University and Ministry of Science and Technology.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.