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4 Building a Roomba Bluetooth Interface
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Requests the ROI to send a packet of sensor data bytes. The user can select one of four different sensor packets. The sensor data packets are explained in more detail in the next section. The ROI must be in passive, safe, or full mode to accept this command. This command does not change the mode. Serial sequence: [142] [Packet Code] Sensors data byte 1: Packet Code (0 3) Speci es which of the four sensor data packets should be sent back by the ROI. A value of 0 speci es a packet with all of the sensor data. Values of 1 through 3 specify speci c subsets of the sensor data. Force-Seeking-Dock Command opcode: 143 Number of data bytes: 0 Turns on force-seeking-dock mode, which causes the robot to immediately attempt to dock during its cleaning cycle if it encounters the docking beams from the Home Base. (Note, however, that if the robot was not active in a clean, spot or max cycle it will not attempt to execute the docking.) Normally the robot attempts to dock only if the cleaning cycle has completed or the battery is nearing depletion. This command can be sent anytime, but the mode will be cancelled if the robot turns off, begins charging, or is commanded into ROI safe or full modes. Serial sequence: [143]
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Kernel machines have been highly successful in solving both classi cation and regression problems. In the particular case of support vector machines, the nonlinear feature space is directly incorporated in the parameter optimization by implicitly mapping the inputs into a new space and nding a suitable linear solution in this feature space. The feature mapping is de ned by a positive de nite kernel function, given by dot products in the feature space but using only the input space patterns. In classi cation, the solution is given by the decision surface, which maximizes the margin separating the classes of patterns in the feature space, where the margin is de ned as the maximal distance between a training example in the feature space and the separating hyperplane. The solution is given by a set of support vectors lying on the boundary, and basically represents the information required to separate the data. The generalization ability is obtained through the margin maximization. For regression problems, the minimization of the weight vector, with a modi ed loss function, is used as a criterion. By solving the quadratic optimization problem, one obtains the number of hidden units, their values, and the weights of the output layer. In the particular case of a support vector machine, a function linear in the parameters is used to approximate the regression in the feature space (6.17): f x
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2 Interfacing Basics
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