In this paper, control algorithms are presented for formation keeping and path followingfor non-holonomic platforms. The controls are based on feedback from onboard directional range sensors, and a switching Kalman filter is introduced for active sensing.Stability is analyzed theoretically and robustness isdemonstrated in experiments and simulations.

In this paper, an optimization-based adaptive Kalman filtering method is proposed. The method produces an estimate of the process noise covariance matrix Q by solving an optimization problem over a shortwindow of data. The algorithm recovers the observations h(x) from a system dot x = f(x), y = h(x) + v without a priori knowledge of system dynamics. Potential applications include target tracking using a network of nonlinear sensors, servoing, mapping, and localization. The algorithm isdemonstrated in simulations on a tracking example for a target with coupled and nonlinear kinematics.Simulations indicate superiority overa standard MMAE algorithm for a large class of systems.

In this paper, a recursive control theoretic smoothing spline approach is proposed forreconstructing a closed contour.Periodic splines are generated by minimizing a cost function subjectto constraints imposed by a linear control system. The optimal controlproblem is shown to be proper, andsufficient optimality conditions are derived for a special case of the problem using Hamilton-Jacobi-Bellman theory.

The filtering effect of the smoothing splines allows for usageof noisy sensor data. An important feature of the method is thatseveral data sets for the same closed contour can be processedrecursively so that the accuracy is improved stepwiseas new data becomes available.

In this paper, a recursive smoothing splineapproach for contour reconstruction is studied and evaluated.  Periodic smoothing splines areused by a robot to approximate the contour of encountered obstaclesin the environment.  The splines are generated through minimizing acost function subject to constraints imposed by a linear controlsystem and accuracy is improved iteratively using a recursive splinealgorithm.  The filtering effect of the smoothing splines allows forusage of noisy sensor data and the method is robust with respect to odometrydrift. The algorithm is extensively evaluated in simulationsfor various contours and in experiments using a SICK laser scanner mounted on a PowerBot fromActivMedia Robotics

In this paper, a method is developed for estimating the optimal smoothing parameter for periodic control theoretic smoothing splines.The procedure is based on general cross validation (GCV) and requires no a priori information about the underlyingcurve or level of noise in the measurements.The optimal smoothin parameter is the minimizer of a GCV cost function, which is derived based on a discretizationof the L₂ smoothing problem for periodic control theoretic smoothing splines.