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This paper is concerned with recent development in robot assisted radiotherapy for lung tumors. The paper first analyzes technical challenges regarding tumor position determination and prediction, by invasive and non-invasive methods, correlation models of the tumor motion and the respiratory signals, as well as modeling and control of the robot manipulator. Several currently available solutions and concepts are then briefly introduced. Emphasis is placed on the ATTS (adaptive tumor tracking system) system developed at the University of Würzburg. Within the ATTS, information of the tumor motion is used to control a robotic treatment couch HexaPOD, which moves in six degrees of freedom and maintains the tumor in a certain fixed spatial position. With this new technology it is possible to avoid gating during the radiotherapy as well as active breathing control, such that duration and cost of the therapy as well as trauma of the patient can be reduced. The tumor position is obtained by data fusion from two sensing system. Correlation methods for data fusion were extensively investigated and a prediction model for tumor motion is found that can forecast the tumor position for up to one respiratory period. Modeling of the HexaPOD treatment couch is addressed. The most suitable model is found to be a low-order linear dynamic equation together with a saturation component. Three different control methods and comparative test results are then presented to show feasibility of the proposed model and control schemes.