Advanced obstacle avoidance for a laser based wheelchair using optimised Bayesian neural networks.
In this paper we present an advanced method of obstacle avoidance for a laser based intelligent wheelchair using optimized Bayesian neural networks. Three neural networks are designed for three separate sub-tasks: passing through a door way, corridor and wall following and general obstacle avoidance. The accurate usable accessible space is determined by including the actual wheelchair dimensions in a real-time map used as inputs to each networks. Data acquisitions are performed separately to collect the patterns required for specified sub-tasks. Bayesian frame work is used to determine the optimal neural network structure in each case. Then these networks are trained under the supervision of Bayesian rule. Experiment results showed that compare to the VFH algorithm our neural networks navigated a smoother path following a near optimum trajectory.
Trieu
Hoang T
HT
University of Technology, Sydney, Broadway, NSW, Australia. thtrieu@eng.uts.edu.au
Nguyen
Hung T
HT
Willey
Keith
K
D000465:Algorithms; D001185:Artificial Intelligence; D001362:Avoidance Learning; D001499:Bayes Theorem; D006233:Persons with Disabilities / Q000534:rehabilitation*; D004867:Equipment Design; D006801:Humans; D008328:Man-Machine Systems; D015233:Models, Statistical; D009068:Movement; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012371:Robotics; D012656:Self-Help Devices; D014910:Wheelchairs