Discussions

By analyzing the experimental results of navigation, some problems are identified as follows.

(1) The result of detecting an object by using the improved snake algorithm might become worse due to the complex background. The control points of the snake will not converge to the edge of the object since the colors of background are too complex. The control point will stop at the edge of background. In the future, the results of the object detection will more satisfactory by improving the snake algorithm.

(2) Object matching is often degraded by the varying lighting condition. Although lighting in indoor environment is more stable than outside, an image still can be affected easily due to the diaphragm of the camera. The vehicle needs to stop more than one second to wait the light steady. Since we use an offsetting technique to overcome it, an erroneous judgment sometimes will occur.

(3) That the floor has to be flat is a constraint of our system. A mechanic error correction model is used in this study but the situation of the vehicle wheel gliding can not be totally overcome. The navigation precision is affected by the roughness of the floor.

Chapter 8

Conclusions and Suggestions for Future Works

8.1 Conclusions

Several techniques and strategies have been proposed in this study and integrated into an autonomous vehicle system for security patrolling in the indoor environments with mechanic error correction and visual object monitoring capabilities.

At first, a setup strategy for the autonomous vehicle is proposed. Two kinds of tasks, namely, Location mapping calibration and mechanic error correction, have been proposed to set up the vehicle before its patrolling. Feasible 2D Location mapping calibration is proposed for acquiring the relative positions between the vehicle and the surrounding environment precisely. The mechanic error correction model which is based on a second-order curve equation is proposed to improve navigation accuracy.

Next, some learning strategies are proposed for the autonomous vehicle, including learning of the planned path and learning of monitoring objects and doors.

The user can easily control the vehicle to navigate in the environment and select monitored objects in the image. And in order to make a precise navigation along a path, one method is to use the coordinates of learned objects as an auxiliary tool to adjust the position and direction of the vehicle. Another method is based on a line following technique. Both ways have been implemented in this study.

In addition, a computer vision process has been proposed for security monitoring

in the navigation path. Several processes, namely, object detection, object recognition, object searching, and door opening detection, have been proposed to detect the current situation during the patrolling process.

The experimental results shown in the previous chapter have revealed the feasibility of the proposed system.

8.2 Suggestions for Future Works

The proposed strategies and methods, as mentioned previously, have been implemented on a small vehicle system. Several suggestions and related interesting issues are worth further investigation in the future. They are described as follows.

(1) Improving the object detection method --- In order to detect monitored object with a more complicated image, the object detection method need be improved, which can then be adopted for more application environments.

(2) Adding the capability of object feature extraction --- This is especially useful when the interesting image regions of an object are hollow. For example, the things are a ring, a wheel, or a flowerpot, etc.

(3) Adding the vehicle abilities of obstacle detection and avoidance such that it can navigate in complex and dynamic environments with objects or humans appearing suddenly on the navigation path.

(4) Adding the ability of human detection and tracking during the vehicle navigation.

(5) Adding the ability of conflagration detection in the house.

(6) Designing a friendlier user-machine interface and simplifying the learning strategy for object and path learning.

(7) Designing a camera system with a capability of panning, tilting, and swinging.

(8) Adding the capability of voice control in the learning process.

(9) Adding the capability of transmitting warning messages from the vehicle to the user’s cell phone by using telecommunication systems.

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