Browsing by Author "Ku, CH"

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Obstacle avoidance for autonomous land vehicle navigation in indoor environments by quadratic classifier
(1999-06-01)
Ku, CH
;
Tsai, WH
;
資訊工程學系
;
Department of Computer Science
A vision-based approach to obstacle avoidance for autonomous land vehicle (ALV) navigation in indoor environments is proposed. The approach is based on the use of a pattern recognition scheme, the quadratic classifier, to find collision-free paths in unknown indoor corridor environments. Obstacles treated in this study include the walls of the corridor and the objects that appear in the way of ALV navigation in the corridor. Detected obstacles as well as the two sides of the ALV body are considered as patterns, A systematic method for separating these patterns into two classes is proposed. The two pattern classes are used as the input data to design a quadratic classifier, Finally, the two-dimensional decision boundary of the classifier, which goes through the middle point between the two front vehicle a heels, is taken as a local collision-free path, This approach is implemented on a real ALV and successful navigations confirm the feasibility of the approach.
Obstacle avoidance in person following for vision-based autonomous land vehicle guidance using vehicle location estimation and quadratic pattern classifier
(2001-02-01)
Ku, CH
;
Tsai, WH
;
資訊工程學系
;
Department of Computer Science
An obstacle avoidance method for use in person following for vision-based autonomous land vehicle (ALV) guidance is proposed. This method is based on the use of vehicle location estimation and a quadratic pattern classifier, and aims to guide the ALV to follow a walking person in front by navigating along a derived collision-free path. Before generating the collision-free path, the person's location is obtained from extracted objects in the image by a person detection method. The object closest to a predicted person location is regarded as the followed person and the remaining objects are regarded as obstacles. The collision-free navigation path is designed for ALV guidance in such a way that the ALV not only can keep following the person but also can avoid collision with nearby obstacles. The navigation path results from a quadratic classifier that uses the vehicle and all of the objects in the image as input patterns. A turn angle is then computed to drive the ALV to follow the navigation path. Successful navigation sessions confirm the feasibility of the approach.
Robust trajectory planning for target following vision-based autonomous land vehicle guidance
(2004-01-01)
Ku, CH
;
Tsai, WH
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交大名義發表
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National Chiao Tung University
We propose a robust trajectory planning method for vision-based autonomous land vehicle (ALV) guidance by target following using a visual field model. The proposed trajectory planning method aims to guide the ALV to achieve the goal that the targeted person will always appear in the image. It is found in this study that the goal can be achieved if three visual contact constraints in the visual field model are satisfied. The first constraint postulates that the target has appeared in the image. The second constraint asks that the vehicle head at the next position points straight towards to the target's current position. The third constraint sets bounds between the next position of the ALV and the current position of the target. A formula for the trajectory of the vehicle that satisfies the second visual contact constraint is derived. The steps for generating a speed and a turn angle of the ALV for real-time navigation are described as a trajectory-planning algorithm. Finally, the approach is tested on a real ALV and successful navigation sessions confirm the feasibility and the robustness of the approach.
Smooth vision-based autonomous land vehicle navigation in indoor environments by person following using sequential pattern recognition
(1999-05-01)
Ku, CH
;
Tsai, WH
;
資訊工程學系
;
Department of Computer Science
A new approach to autonomous land vehicle (ALV) navigation by the person following is proposed. This approach is based on sequential pattern recognition and computer vision techniques, and maintenance of smoothness for indoor navigation is the main goal. The ALV is guided automatically to follow a person who walks in front of the vehicle. The vehicle can be used as an autonomous handcart, go-cart, buffet car, golf cart, weeder, etc. in various applications. Sequential pattern recognition is used to design a classifier for making decisions about whether the person in front of the vehicle is walking straight or is too right or too left of the vehicle. Multiple images in a sequence are used as input to the system. Computer vision techniques are used to detect and locate the person in front of the vehicle. By sequential pattern recognition, the relation between the location of the person and that of the vehicle is categorized into three classes. Corresponding adjustments of the direction of the vehicle are computed to achieve smooth navigation. The approach is implemented on a real ALV, and successful and smooth navigation sessions confirm the feasibility of the approach. (C) 1999 John Wiley & Sons, Inc.