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The subject of this paper is the motion control problem of wheeled mobile robots (WMRs) in environments without obstacles. With reference to the popular unicycle kinematics, it is shown that dynamic feedback linearization is an efficient design tool leading to a solution simultaneously valid for both trajectory tracking and setpoint regulation problems. The implementation of this approach on the laboratory prototype SuperMARIO, a two-wheel differentially driven mobile robot, is described in detail. To assess the quality of the proposed controller, we compare its performance with that of several existing control techniques in a number of experiments. The obtained results provide useful guidelines for WMR control designers.

In this paper, we develop the switching controller presented by Lee et al. [Lee et al., 1999] for the pose control of a car-like vehicle, to allow the use of an omnidirectional vision sensor. To this end we incorporate an extension to a hypothesis on the navigation behaviour of the desert ant, cataglyphis bicolor, which leads to a correspondence free landmark based vision technique. The method we present allows positioning to a learnt location based on feature bearing angle and range discrepancies between the robot’s current view of the environment, and that at a learnt location. We present simulations and experimental results, the latter obtained using our outdoor mobile platform. 1

A closed loop, time-invariant and globally stable control law for a bicycle-like kinematic model is proposed. The resulting paths are smooth and the curvature bounded on the whole state space trajectories. As linear velocity can be kept arbitrary small, thus avoiding large lateral accelerations and actuator saturation problems, it is suggested that the proposed law may be also adopted for the planar control of autonomous underwater vehicles. The target configuration is always approached on a straight line and the vehicle is requested to move in only one specified forward direction thus avoiding cusps in the paths and satisfying a major requirement for the implementation of such strategy on many real systems. 1 Introduction The main di#culty in the stabilization of a large class of nonholonomic systems is related to the theorem of Brockett [1] that shows how these systems cannot be stabilized by time-invariant smooth state feedback. A prototype of such systems is given by the cartesia...

It is shown that, adopting a polar-like kinematic description of the Cartesian unicycle nonholonomic vehicle in order to prevent Brocketts negative result, a stabilizing time-invariant feedback law can be simply obtained projecting a suitable holonomic linear velocity on the nonholonomic linear velocity axis and superimposing an angular velocity that asymptotically steers such axis parallel to the holonomic velocity vector. KEYWORDS: Nonholonomic vehicles, unicycle model INTRODUCTION The problem of feedback stabilizing the nonholonomic unicycle-like system has received a wide attention in the literature of the last years. This was due both to the concern of the robotics community dealing with the practical control of nonholonomic vehicles and to the theoretical interest inspired by the work of Brockett [1] that shows how a large class of nonholonomic systems cannot be stabilized by timeinvariant smooth state feedback. Nevertheless thanks to the results presented in [2] [3] [4] [5] it i...

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This paper addresses the state estimation of systems with perspective outputs. We derive a minimum-energy estimator which produces an estimate of the state that is “most compatible” with the dynamics, in the sense that it requires the least amount of noise energy to explain the measured outputs. Under suitable observability assumptions, the estimate converges globally asymptotically to the true value of the state in the absence of noise and disturbance. In the presence of noise, the estimate converges to a neighborhood of the true value of the state. These results are also extended to solve the estimation problem when the measured outputs are transmitted through a network. In that case, we assume that the measurements arrive at discrete-time instants, are time-delayed, noisy, and may not be complete. We show that the re-designed minimum-energy estimator preserves the same convergence properties. We apply these results to the estimation of position and orientation for a mobile robot that uses a monocular charged-coupled-device (CCD) camera mounted onboard to observe the apparent motion of stationary points. In the context of our application, the estimator can deal directly with the usual problems associated with vision systems such as noise, latency and intermittency of observations. Experimental results are presented and discussed.

A new image-based visual servoing algorithm is presented for nonholonomic mobile robots. The algorithm, based on epipolar geometry, consists of three independent and sequential steps making use of both the estimated epipoles and the image features. In particular, due to the nonlinear dynamics of the camera-robot system, an input-output feedback linearizing control law is used during the second step. Simulations results are presented to validate the proposed visual servoing technique. I.

In this paper the problem of stabilizing a wheeled vehicle of unicycle type to a set point, using only visual feedback, is considered. The practically most relevant problem of keeping the tracked features in sight of the camera while maneuvering to park the vehicle is taken into account. This constraint, often neglected in the literature, combines with the nonholonomic nature of the vehicle kinematics in a challenging controller design problem. We provide an e#ective solution to such problem by using a combination of previous results on non-smooth control synthesis, and recently developed hybrid control techniques. Simulations and experimental results on a laboratory vehicle are reported, showing the practicality of the proposed approach.

This paper addresses the problem of regulating the dynamic model of a nonholonomic wheeled robot of the unicycle type to a point with a desired orientation. A simple controller is derived that yields global convergence of the trajectories of the closed loop system in the presence of parametric modeling uncertainty. Controller design relies on a non smooth coordinate transformation in the original state space, followed by the derivation of a Lyapunov-based, adaptive, smooth control law in the new coordinates. Convergence to the origin is analyzed and simulation results are presented.