In this paper we present a method of shared control strategy for an intelligent wheelchair to assist a disable user in performing obstacle avoidance tasks. The system detects obstacles in front of the wheelchair using a laser range finder sensor. As the wheelchair moves the information from the laser range finder is combined with data from the encoders mounted in its driving wheels to build a 360 degrees real-time map. The accuracy of the map is improved by eliminating the systematic error that would result from both the uncertainty of effective wheelbase and unequal driving wheel diameters. The usable wheelchair accessible space is determined by including the actual wheelchair dimensions in producing the real-time map. In making a decision the shared control method considers the user's intentions via the head-movement interface, accessible space of the environment and user safety. The experiments show promising results in the intelligent wheelchair system.
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.
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 free-space 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.
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.
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