Obstacle avoidance for power wheelchair using Bayesian neural network

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 29th International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 4771 - 4774
Issue Date:
2007-01
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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.
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