Implementation of a shared control system for brain-controlled wheelchair navigation

of the Thesis Implementation of a shared control system for brain-controlled wheelchair navigation by Rui Luo Master of Science in Electrical and Computer Engineering Northeastern University, Feb 2018 Taşkın Padır, Adviser Individuals with physical disabilities continue to rely on electric wheelchairs and personalized human-machine interfaces for their mobility. Even though the problem is well-studied in literature, the development of reliable shared control paradigms that supports different low throughput human machine interfaces for semi-autonomous wheelchairs has been a challenging problem. This work focuses on enhancing the shared position control methodology known as NoVeLTI (Navigation via Low Throughput Interfaces) in four areas. (1) A new wheelchair orientation controller with user interface has been developed. This controller infers user’s desired orientation of the wheelchair from detected commands using Bayes filter and then generates control commands to rotate the wheelchair after the desired position is reached; (2) The ROS implementation of the system architecture is redesigned to ensure a stable communication channel among the system modules rather than relying on ROS topic; (3)A nonholonomic robot model has been implemented in the original system to simulate the performance of a wheelchair in real environment; (4) An improved user interface has been designed and implemented in RViz according to feedback from the attenders of our simulation experiments . A set of experiments are designed to validate the improvements and evaluate the effect of different parameters in the system. Four human experiments were conducted. Result shows that subjects were able to navigate the wheelchair via only four commands from simulated BCI model to any given destination on a map at an average success rate of 90%. The average navigation time of a 25m route was about 60 seconds at the driving velocity of 1m/s.