Browsing by Author "Chang, Po-Ju"

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An Intelligent Wheelchair Anti-Lock Braking System Design With Friction Coefficient Estimation
(2018-01-01)
Wu, Bing-Fei
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Chang, Po-Ju
;
Chen, Yung-Shin
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Huang, Ching-Wei
;
電機工程學系
In the future, the population's aging problem will become increasingly serious. For medical care, action-assisted vehicles are one of the solutions. A wheelchair is a common mobility assistance equipment, and the safety of the user is an important issue. For the ordinary wheelchairs, most of them use a fixed hand brake or disk brake to allow the users to stop the wheelchair. However, improper use of brakes tends to cause slippage, and there is no concept of the anti-lock braking system (ABS) for the wheelchair. Therefore, this paper applies this idea to intelligent wheelchairs with the aim of enhancing the safety of users in wheelchairs. The ABS architecture includes the core algorithms of the adaptive fuzzy-neural inference system and the friction coefficient estimation system. The friction coefficient estimation system uses a particle filter to quickly adapt to a non-linear state and unknown environment. The system provides more accurate braking control for the ABS according to the range of the friction coefficient based on the pavement type. In ABS, it uses the gyroscope to detect the acceleration and wheelchair angle information and then calculates the parameters of the wheelchair. The user clicks the stop command on the wheelchair to activate the brake system to achieve a simple and efficient mode of operation. It can efficiently reduce the braking time and braking distance and enhance the riding safety of wheelchairs.
An Uphill Safety Controller With Deep Learning-Based Ramp Detection for Intelligent Wheelchairs
(2018-01-01)
Wu, Bing-Fei
;
Chen, Yung-Shin
;
Huang, Ching-Wei
;
Chang, Po-Ju
;
電控工程研究所
In a society with aging population, the demand for electric wheelchairs is growing with the advancement of automation. However, many accidents have occurred due to the misjudgment of the slope angle and wheelchair speed while the wheelchair is traveling on ramps. This research employs the light electronic assistance pal compact motor package to reduce the weight and size of conventional electric wheelchairs. The modular design of proposed uphill controller and ramp detection functions allows users to easily select and incorporate only the functions they need. This paper proposes a ramp detection model implemented using the deep learning algorithm with CNN-4 structure to analyze depth image data. The model's recognition time of each video frame is 11 times faster than that of the AlexNet and GoogleNet. The uphill safety controller is designed as an adaptive network-based fuzzy inference system with Q-learning. The safe speed is automatically calculated according to the angle obtained from slope classification and revised in real-time during the slope driving to prevent the user from moving towards the dangerous ramp or rolling back due to inadequate speed. The accuracy of ramp detection is further increased by 5% to 97.1% due to assistance from the voting system processing and the gyroscope output data. The 5 degrees ramp experiment of our uphill controller with ramp classification takes 20 s to complete the slope driving which is 23% faster than the controller without ramp detection. The energy consumption is also one half less than the experiment without uphill detection.