Abstract:
In this paper we present a real-time obstacle
avoidance algorithm using a Bayesian neural network for a
laser based wheelchair system. The raw laser data is modified
to accommodate the wheelchair dimensions, allowing the freespace
to be determined accurately in real-time. Data
acquisition is performed to collect the patterns required for
training the neural network. A Bayesian frame work is applied
to determine the optimal neural network structure for the
training data. This neural network is trained under the
supervision of the Bayesian rule and the obstacle avoidance
task is then implemented for the wheelchair system. Initial
results suggest this approach provides an effective solution for
autonomous tasks, suggesting Bayesian neural networks may
be useful for wider assistive technology applications.
