Recent experiments on sensory-motor systems of frogs and rats have revealed that those systems have a modular structure. Apparently, control actions form a vector space with a handful of elementary controls as a basis. The reduction of admissible controls to a vector space plays in control theory a similar role to PCA in learning and recognition applications. Inspired by these observations, we first propose a mathematical model of the experimentally observed modular structure. We then show how to choose elementary control actions so as to make the system reach any desired final state. Finally, we determine the minimum number of elementary control actions to perform reaching.

Sensing the load dynamic in an overhead crane is not a simple task because both load angular position and velocity measurements are not easily attainable and subject to disturbances. A method to reconstruct the dynamic of the load is presented. It is based on the estimate of the torque that the load produces on the drive that governs the translational dynamic of the trolley at which the load is hanging. It requires a minimal sensor equipment, namely measures of the electrical torque and angular velocity of the drive, as both swing angle and load speed are shown to be observable for the linearized model.

An algorithm that performs recursive estimation of ego-motion and ambient structure from a stream of monocular perspective images of a number of feature points is presented. The algorithm is based on an extended Kalman filter (EKF) that integrates over time the instantaneous motion and structure measurements computed by a two-perspective-views step. The key features of the authors' filter are: global observability of the model, and complete online characterization of the uncertainty of the measurements provided by the two-views step. The filter is thus guaranteed to be well-behaved regardless of the particular motion undergone by the observer. Regions of motion space that do not allow recovery of structure (e.g., pure rotation) may be crossed while maintaining good estimates of structure and motion. Whenever reliable measurements are available they are exploited. The algorithm works well for arbitrary motions with minimal smoothness assumptions and no ad hoc tuning. Simulations are presented that illustrate these characteristics.>