Learning Mobility Aid Assistance via Decoupled Observation Models

Publication Type:
Conference Proceeding
2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018, 2018, pp. 1903 - 1910
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ICARCV18.pdfAccepted manuscript4.99 MB
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© 2018 IEEE. This paper presents an active assistance framework for mobility systems, such as Power Mobility Devices (PMD), with the distinctive goal of being able to operate within a local moving window, as opposed to the common reliance upon persistent global environments and objectives. Demonstration data from able experts driving a simulated mobility aid in a representative indoor setting is used off-line to build behavioral models of navigation postulated separately upon user joystick inputs and on-board sensor data. These models are built respectively via Gaussian Processes for the joystick signals, and a Deep Convolutional Neural Network for the sensor data; in this case a planar LIDAR. Their combined outputs form a continuous distribution of estimated traversal likelihood within the user's immediate space, allowing for real-time stochastic optimal path planning to guide a user to its intended local destination. Moreover, the computational efficiency of the decoupled models permits rapid replanning on-the-fly for a smooth assistive action. On-line and off-line evaluations substantiate the advantages of the framework in generalising intelligent navigational assistance, of particular relevance for users who experience difficulty in safe mobility.
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