Adaptive shared control strategies based in the Bayesian recursive technique in an intelligent wheelchair.
In this paper we present an adaptive shared control method for an intelligent wheelchair based on the Bayesian recursive technique to assist a disable user in performing obstacle avoidance tasks. Three autonomous tasks have been developed for different types of environments to improve the performance of the overall system. The system combines local environmental information gathered using a laser range finder sensor with the user's intentions to select the most suitable autonomous task in different situations. The evidences of these tasks are estimated by the Bayesian recursive technique during movements of the wheelchair. The most appropriate task is chosen to be the with the highest evidence value. Experimental results show significant performance improvements compared to our previously reported shared control methods.
Trieu
Hoang T
HT
Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway, NSW 2007, Australia. thtrieu@eng.uts.edu.au
Willey
Keith
K
Nguyen
Hung T
HT
D001185:Artificial Intelligence; D001499:Bayes Theorem; D001698:Biomedical Engineering; D004867:Equipment Design; D006801:Humans; D014910:Wheelchairs / Q000706:statistics & numerical data