Bayesian recursive algorithms for estimating freespace and user intention in a semi-autonomous wheelchair with a stereoscopic camera system
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NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Assistive technologies have been proposed in recent years to install in mobile wheelchairs for providing severely disabled people with enhanced access to independent activities in their daily life. In practice, ultrasound and laser sensors have been developed to navigate obstacles, but they only provide two-dimensional (2D) grids. The main contributions of this thesis are in the exploitation of three-dimensional (3D) information from the stereoscopic camera in the estimation of free space and the user’s intention. This is achieved through using Bayesian Recursive (BR) algorithms which are conditioned on measurements, control data and conditional probabilities, within a semi-autonomous wheelchair control system. In order to provide 3D information for detecting free spaces and obstacles, a “Bumblebee” stereoscopic camera system has been mounted to a powered wheelchair. The Sum of Absolute Differences (SAD) algorithm is subsequently used for constructing an optimal disparity map. Especially, the color intensity functions of images have been applied to obtain this optimal disparity map. Moreover, the mark size and the disparity boundaries are designed to increase the optimality of the disparity map. Given the optimal disparity map, both 3D point and 2D distance maps are produced for controlling the autonomous wheelchair. In particular, the height and width of free space have been computed for passing through. Experimental results have shown the effectiveness of the SAD approach using the color intensity function and benefits of computing 3D point map and 2D distance map for the wheelchair control. The stereoscopic camera system may provide 3D information about free spaces in the environment. However, the free space information can be uncertain. This is especially likely when the free space height and/or width are close to the safe height and/or diameter of the wheelchair. It is then difficult for the wheelchair to estimate the height and/or width for moving through free space. To combat this, the BR algorithm is applied for estimating free space. In order to apply a Bayesian decision for the wheelchair to pass autonomously through free space, the average optimal probability values are determined. Experimental results for estimating various free spaces prove that the proposed BR approach is effective. A semi-autonomous wheelchair control strategy is proposed to combine between user intention and autonomous mode for the safe and comfortable control of the user. In the autonomous mode, a dynamic free space has been estimated using the advanced BR algorithm conditioned on the “obstacle” distance. This can be altered when a moving obstacle is in front of the mobile wheelchair. User intentions are often uncertain due to noise from a head-movement sensor, and it is therefore difficult for the mobile wheelchair to determine user intentions. Hence, the advanced BR algorithm is utilized, conditioned on dynamic free space estimation. The algorithm is used to determine the user intention. In conclusion, the experimental results detailed in the thesis, serve to illustrate the effectiveness of the approaches.
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