Discussions

The proposed system utilizes multiple vision-based autonomous vehicles to perform the security patrolling task. For this purpose, some monitoring points are utilized to guide the vehicles. By the way, there are more applications of the monitoring points, such as providing various services. Every monitoring point can be regarded, for example, as a business service point in which there are some customers.

If the environment is a restaurant, the apparatus of showing menu can be equipped on the vehicles, and then the vehicles can move to each service point along assigned optimal paths to ask what dishes or services are needed. If the environment is a company, the vehicles also can be utilized to deliver documents or messages in each service point. Furthermore, if a walkable area can be divided into many ranges, in which each is within the controllable view of the vehicle, we may transform every range into a node such that the vehicles can arrive at anywhere in it to do some actions, such as detecting whether an unknown person has invaded with optimal randomized paths.

However, there are still some problems in the system. If an object appears next to the vehicle suddenly, the top-view omni-cameras will not have the ability to find out the vehicle. To solve the problem, it might be necessary to add information of color and sample models of the vehicles to this system. Furthermore, the vehicles are not on a plane, so the vehicle localization accuracy is affected by the heights of the vehicles.

If the vehicle is taller and farther from the top-view omni-cameras, the error between the obtained centroid and the actual position of the vehicle will be large. However, we might be able to add an obvious mark on the center of the top of the vehicle. By finding the mark, the correct position can also be obtained. Finally, the proposed real-time collision avoidance technique between vehicles is feasible for two vehicles.

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If the total number of vehicles is larger than two, we will need to consider the influence of passing points for the third vehicle. The problem is worth for future research.

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Chapter 8

Conclusions and Suggestions for Future Works

8.1 Conclusions

In this study, we utilize multiple vision-based autonomous vehicles to develop a security patrolling system in an environment whose floor shape is composed of rectangular regions. We have proposed several techniques and adopt some algorithms which are summarized in the following.

(1) An environment-information calculation method has been proposed, by which we can obtain all rectangular regions, which form the floor shape of the patrolling environment, the turning points, and then all between-MP distances and paths.

The turning points are utilized to enable the vehicles to move between any pair of MPs without collisions with the walls. With the turning points, we adopt Dijkstra’s algorithm to obtain the shortest between-MP distances and paths between the two MPs which belong to the different regions.

(2) A point-correspondence technique integrated with an image interpolation method for camera calibration has been proposed. In this study, we don’t use the traditional projection-based transformation. Instead, a grid pattern is used as the calibration target and corresponding points between 2-D image and 3-D global spaces are utilized. For the warped images captured by the top-view omni-cameras, the correct coordinate positions can be obtained by the

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corresponding points and the use of an image interpolation method.

(3) A faster point-correspondence technique has been proposed. Because more corresponding points will yield better calibration accuracy, we adopt a minimum mean square error (MMSE) method to calculate quadratic curves and abundant cross points, in the image captured by the top-view omni-camera, can be obtained. Each cross point and its coordinates in the global space, obtained by an interpolation method, are exactly one pair of point correspondences.

(4) A vehicle-pose learning method has proposed, by which the vehicles are taught where and in which direction to perform the security monitoring task, which is to take pictures of monitored objects as defined in this study. Furthermore, the learned positions can be utilized to guide the vehicles.

(5) An optimal method for randomized and load-balanced path planning has been proposed, in which each MP is just passed once such that monitored objects can be patrolled uniformly. Additionally, the difference of the numbers of assigned MPs for all vehicles is smaller and a threshold distance is set to restrict the difference between path distances, so that the loads of all vehicles can be balanced. According to the numbers of assigned MPs, the MPs are chosen randomly, and then the system calculate the shortest paths with each MP on these paths appearing only once by the concept of the TSP.

(6) A vehicle localization and monitoring method has been proposed. Because the vehicles suffer from mechanic errors, we utilize the top-view omni-cameras to locate them in this study. By the odometer values of the vehicles, we can calculate the centroids of the vehicles in the image. After the centroids are transformed into the global space, the odometer values are corrected by the coordinates of the resulting points. Besides, the directional angles of the vehicles also must be corrected, in which two continuous correct position points are

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utilized to do the job. Additionally, the cameras have the ability to monitor vehicles to see whether they are still under control. If any vehicle loses control of its action, the system will send an alarm message to the security center and stop all vehicles.

(7) A real-time collision avoidance technique between two vehicles has been proposed. By the odometer values, the system computes the distance between two vehicles in every cycle of a fixed-time duration and determines whether they are too close. If yes, the feasible alternative paths of the vehicles will be obtained by two different kinds of states, path-intersecting or non-path-intersecting.

The experimental results shown in the previous chapters 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 vehicle system with multiple vision-based autonomous vehicles.

According to this study, in the following we make several suggestions and point out some related interesting issues, which are worth further investigation in the future:

(1) using a pen-tilt-zoom camera equipped on the vehicle to capture clearer images, and then extracting features of the images to detect whether monitored objects still exist;

(2) adding the capability to detect more danger conditions;

(3) adding the capability of warning users immediately through cell phones or electronic mails;

(4) adding the capability of voice control when users want to issue navigation orders

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to the vehicle;

(5) improving the real-time collision avoidance technique to be suitable for more vehicles; and

(6) improving the accuracy of finding the centroid of the vehicle.

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References

[1] I. Fukui, “TV image processing to determine the position of a robot vehicle,”

Pattern Recognition, vol. 14, pp. 101-109, 1981.

[2] Betke M and Gurvits L, “Mobile robot localization using landmarks,” IEEE Transactions on Robotics and Automation, vol. 13, no 2, pp 251-263,Apr., 1997.

[3] M. J. Magee and J. K. Aggarwal, “Determining the position of a robot using a single calibration object,” IEEE Conference on Robotics, pp. 57-62, Atlanta, Georgia, USA, May 1983.

[4] J. Huang, C. Zhao, Y. Ohtake, H. Li, and Q. Zhao, “Robot position identification using specially designed landmarks,” Proceedings of 2006 IEEE Conference on Instrumentation and Measurement Technology, Italy, Apr., 2006.

[5] H. L. Chou and W. H. Tsai “A new approach to robot location by house corners,”

Pattern Recognition, vol. 19, pp. 439-451, 1986.

[6] K. L. Chiang and W. H. Tsai, “Vision-based autonomous vehicle guidance in indoor environments using odometer and house corner location information,”

Proceedings of 2006 IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 415-418, USA, Dec. 18-20, 2006.

[7] K. C. Chen and W. H. Tsai, “A study on autonomous vehicle navigation by 2D object image matching and 3D computer vision analysis for indoor security patrolling applications,” Proceedings of 2007 Conference on Computer Vision, Graphics and Image Processing, Miaoli, Taiwan, June, 2007.

[8] D. Cobzas, H. Zhang, and M. Jagersand, “Image-based localization with depth-enhanced image map,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA 2003), pp. 1570-1575, Taipei, Taiwan, 2003.

[9] P. Biber, H. Andreasson, T. Duckett, and A. Schilling, ”3D modeling of indoor

115

environments by a mobile robot with a laser scanner and panoramic camera,”

Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), Sendai, Japan, Sept. 28 - Oct. 2, 2004.

[10] C. C. Lai and W. H. Tsai, “A study on automatic indoor navigation techniques for vision-based mini-vehicle with off-line environment learning capability,”

Proceedings of 2003 Conference on Computer Vision, Graphics and Image Processing, Kinmen, Taiwan, June, 2003.

[11] N. Winters and J. Santos-Victor, “Mobile robot navigation using omni-directional vision,” Proceedings of. 3rd Irish Machine Vision and Image Processing Conference (IMVlP'99), Dublin, Ireland, 1999.

[12] L. E. Kavraki,J. C. Latombe, P. vestka, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Transactions on Robotics and Automation, vol. 12, no. 4, Aug., 1996.

[13] R. Kimmel, N. Kiryati, and A. M. Bruckstein, "Multivalued distance maps for motion planning on surfaces with moving obstacles," IEEE Transactions on Robotics and Automation, vol. 14, no. 3, pp. 427-436, June, 1998.

[14] Y. Mezouar and F. Chaumette, “Path planning for robust image-based control,”

IEEE Transactions on Robotics and Automation, vol. 18, no. 4, Aug., 2002.

[15] S. Hert and B. Richards, “Multiple-Robot Motion Planning = Parallel Processing + Geometry,” Springer-Verlag, pp. 195-215, London, UK, 2000.

[16] M. C. Chen and W. H. Tsai “Vision-based security patrolling in indoor environments using autonomous vehicles,” Proceedings of 2005 Conference on Computer Vision, Graphics and Image Processing, Taipei, Taiwan, Aug., 2005.

[17] D. Parsons and J. Canny, "A motion planner for multiple mobile robots," IEEE International Conference on Robotics and Automation, vol. 1, pp. 8-13, May 13-18, 1990.

116

[18] M. Bennewitz and W. Burgard, “Coordinating the motions of multiple mobile robots using a probabilistic Model,” Proceedings of the International Symposium on Intelligent Robotic Systems (SIRS), England, 2000.

[19] F. M. Marchese and M. D. Negro, ” Path-Planning for Multiple Generic-shaped Mobile Robots with MCA,” Springer-Verlag Ed., vol. 3993, pp 264-271, Berlin, Heidelberg(D), May, 2006.

[20] N.L.J. Ulder, E. H. L. Aarts, H. J. Bandelt, P. J. M. Van Laarhoven, and E. Pesch,

“Genetic local search algorithms for the traveling salesman problem,” Springer, Parallel Problem Solving from Nature––Proceedings of 1st Workshop , vol. 496, pp. 109-116, Berlin, Germany, 1991.

[21] S. Tschoke,R. Lubling, and B .Monien, “Solving the Traveling Salesman Problem with a Distributed Branch-and-Bound Algorithm on a 1024 Processor Network,” Proceedings of International Parallel Processing Symposium, April 25-28, 1995, pp. 182-189.

[22] J. W. Pepper, B. L. Golden, and E. A. Wasil, “Solving the traveling salesman problem with annealing-based heuristics: A computational study,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 32, no. 1, Jan., 2002.

[23] D. A. Forsyth and J. Ponce, Computer Vision: A Modern Approach, Prentice Hall, Upper Saddle River, New Jersey, USA, 2002.

[24] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 2nd edition. MIT Press, Cambridge, 2001.