While the nonholonomic robots with no-slipping constraints are studied extensively nowadays, the slipping effect is inevitable in many practical applications and should be considered necessarily to achieve autonomous navigation and control purposes especially in outdoor environments. In this paper the robust point stabilization problem of a tracked mobile robot is discussed in the presence of track slipping, which can be treated as model perturbation that violates the pure nonholonomic constraints. The kinematic model of the tracked vehicle is created, in which the slipping is assumed to be a time-varying pa- rameter under certain assumptions of track-soil interaction. By transforming the original system to the special chained form of nonholonomic system, the integrator backstepping procedure with a state-scaling technique is used to construct the controller to stabilize the system at the kinematic level. The global exponential stability of the final system can be guaranteed by Lyapunov theory. Simulation results with different initial states and slipping parameters demonstrate the fast convergence, robustness and insensitivity to the initial state of the proposed method.
A probabilistic algorithm is proposed for the problem of simultaneous robot localization and peopletracking （SLAP） using single onboard sensor in situations with sensor noise and global uncertainties over the observer＇s pose. By the decomposition of the joint distribution according to the Rao-Blackwell theorem, posteriors of the robot pose are sequentially estimated over time by a smoothed laser perception model and an improved resampling scheme with evolution strategies; the conditional distribution of the person＇s position is estimated using unscented Kalman filter （UKF） to deal with the nonlinear dynamic of human motion. Experiments conducted in a real indoor service robot scenario validate the favorable performance of the positional accuracy as well as the improved computational efficiency.
