A Study on Optimal Security Patrolling by Multiple Vision-Based Autonomous Vehicles with Omni-Monitoring
A multiple vision-based vehicle system for security patrolling in an indoor environment, whose floor shape is composed of rectangular regions, is proposed. Two autonomous vehicles controllable by wireless communication and equipped with cameras, as well as two cameras with fish-eye lenses fixed on the ceiling, are used as a test bed. To acquire information of an unknown environment, an environment-information calculation method is proposed for obtaining all rectangular regions composing the floor shape of the environment, the turning points for navigation, all distances between monitored objects, and the patrolling paths. These data enable the vehicles to navigate without collisions with walls. Also, a point-correspondence technique integrated with an image interpolation method is proposed for camera calibration. By a technique of finding corresponding points in 2-D image and 3-D global spaces as well as an image interpolation method, the correct positions of interesting feature points can be obtained from the warped images captured by the cameras with fish-eye lenses. Besides, a faster point-correspondence technique is proposed to obtain abundant corresponding points that yield better calibration accuracy. With this camera calibration technique, the cameras on the ceiling can be utilized to learn the poses of the vehicles with respect to monitored objects. Also, the vehicles are taught where and in which direction to perform the security monitoring task, in which the position information is used to guide the vehicles. Additionally, the top-view cameras can also be utilized to locate the vehicles and monitor vehicle activities in the navigation phase. An optimal randomized and load-balanced path planning method is proposed as well, which requires shorter time to accomplish object monitoring in one session and provides higher degrees of patrolling security. Because the number of the vehicles used in this study is more than one, a real-time collision avoidance technique is also proposed. According to the state of path-intersecting, feasible alternative paths for the vehicles can be obtained. Good experimental results show the flexibility and feasibility of the proposed methods for the application of multiple-vehicle security patrolling.
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