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We present an initial examination of a novel approach to accurately position a patient during head and neck intensity modulated radiotherapy (IMRT). Position-based visual-servoing of a radio-transparent soft robot is used to control the flexion/extension cranial motion of a manikin head. A Kinect RGB-D camera is used to measure head position and the error… (More)
This work presents an on-going investigation of the control of a pneumatic soft-robot actuator addressing accurate patient positioning systems in maskless head and neck cancer radiotherapy. We employ two RGB-D sensors in a sensor fusion scheme to better estimate a patient's head pitch motion. A system identification prediction error model is used to obtain… (More)
We review the above-mentioned paper by [Bhat et. al., '98] where a class of bounded, continuous time-invariant finite time stabilizing feedback laws are derived for the double integrator and Lya-punov theory is employed in establishing finite-time convergence.
— Neural networks are known to be effective function approximators. Recently, deep neural networks have proven to be very effective in pattern recognition, classification tasks and human-level control to model highly nonlinear real-world systems. This paper investigates the effectiveness of deep neural networks in the modeling of dynamical systems with… (More)