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° 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.
- Command and control systems
- adaptive network-based fuzzy inference system (ANFIS)
- deep learning
- intelligent wheelchair
- ramp classification