1,720,982 research outputs found
Vision-based range estimation via Immersion and Invariance for robot formation control
No full textThe paper introduces a new vision-based range estimator based upon the Immersion and Invariance (I&I) methodology, for leader-follower formation control. The proposed reduced-order nonlinear observer achieves global exponential convergence of the observation error to zero and it is extremely simple to implement and to tune. A Lyapunov analysis is provided to show the stability of the closed-loop system arising from the combination of the range estimator and an input-state feedback controller. Simulation experiments illustrate the theory and show the effectiveness of the proposed design
On visibility maintenance via controlled invariance for leader-follower Dubins-like vehicles
No full textThe paper studies the visibility maintenance problem (VMP) for a leader-follower pair of robots modelled as first-order dynamic systems and proposes an original solution based on the notion of controlled invariance. The nonlinear model describing the relative dynamics of the vehicles is interpreted as linear uncertain system, with the leader robot acting as an external disturbance. The VMP can then be reformulated as a linear constrained regulation problem with additive disturbances (DLCRP). New positive D-invariance conditions for linear uncertain systems with parametric disturbance matrix are introduced and used to solve the VMP when box bounds on the state, control input and disturbance are considered. The proposed design procedure can be easily adapted to provide the control with UBB disturbances rejection capabilities. As an extension, the paper addresses the VMP on a circle. Simulation experiments show the effectiveness of the proposed designs
Observer design via Immersion and Invariance for vision-based leader-follower formation control
No full textThe paper introduces a new vision-based range estimator for leader-follower formation control, based upon the Immersion and Invariance (I&I) methodology. The proposed reduced-order nonlinear observer is simple to implement, easy to tune and achieves global asymptotical convergence of the observation error to zero. Observability conditions for the leader-follower system are analytically derived by studying the singularity of the Extended Output Jacobian. The stability of the closed-loop system arising from the combination of the range estimator and an input-state feedback controller is proved by means of Lyapunov arguments. Simulation experiments illustrate the theory and show the effectiveness of the proposed designs. (C) 2009 Elsevier Ltd. All rights reserved
Sliding mode formation tracking control of a tractor and trailer-car system
No full textIn this paper a new leader-follower formation of nonholonomic mobile robots is studied. The follower is a car-like vehicle and the leader is a tractor pulling a trailer. The leader moves along assigned trajectories and the follower is to maintain a desired distance and orientation to the trailer. A sliding mode control scheme is proposed for asymptotically stabilizing the vehicles to a time-varying desired formation. The attitude angles of the follower and the tractor are estimated via global exponential observers based on the invariant manifold technique. Simulation experiments illustrate the theory and show the effectiveness of the proposed formation controller and nonlinear observers
Range estimation from a moving camera: an Immersion and Invariance approach
No full textThe paper proposes an original solution to the range identification problem for perspective dynamical systems. The depth of a static point observed by a pinhole camera undergoing a predefined 3-D motion, is estimated from its 2-D projection on the image plane. The proposed nonlinear observer relies on the Immersion and Invariance (I&I) methodology and offers several advantages over the existing range estimators. The paper also provides an analytical study of nonlinear observability performed with the Extended Output Jacobian. Extensive simulation experiments illustrate the theory and show the effectiveness of the proposed design
KCT: a MATLAB toolbox for motion control of KUKA robot manipulators
No full textThe Kuka Control Toolbox (KCT) is a collection of MATLAB functions for motion control of KUKA robot manipulators, developed to offer an intuitive and high-level programming interface to the user. The toolbox, which is compatible with all 6 DOF small and low payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via TCP/IP. KCT includes more than 30 functions, spanning operations such as forward and inverse kinematics computation, point-to-point joint and Cartesian control, trajectory generation, graphical display and diagnostics. The flexibility, ease of use and reliability of the toolbox is demonstrated through two applicative examples
Leader–follower formation control of nonholonomic mobile robots with input constraints
No full textThe paper deals with leader–follower formations of nonholonomic mobile robots, introducing a formation control strategy alternative to those existing in the literature. Robots’ control inputs are forced to satisfy suitable constraints that restrict the set of leader possible paths and admissible positions of the follower with respect to the leader. A peculiar characteristic of the proposed strategy is that the follower position is not rigidly fixed with respect to the leader but varies in proper circle arcs centered in the leader reference frame
Application of Kalman filter to remove TMS-induced artifacts from EEG recordings
No full textTranscranial magnetic stimulation (TMS) is a technique in which a pulsed magnetic field created by a coil positioned next to the scalp is used to locally depolarize neurons in brain cortex. TMS can be combined with electroencephalography (EEG) to visualize regional brain activity in response to direct cortical stimulation, making it a promising tool for studying brain function. A technical drawback of EEG/TMS coregistrations is that the TMS impulse generates high amplitude and long-lasting artifacts that corrupt the EEG trace. In this brief, an offline Kalman filter approach to remove TMS-induced artifacts from EEG recordings is proposed. The Kalman filter is applied to the linear system arising from the combination of the dynamic models describing EEG and TMS signals generation. Time-varying covariance matrices suitably tuned on the physical parameters of the problem allow us to model the non-stationary components of the EEG/TMS signal, (neglected by conventional stationary filters). Experimental results show that the proposed approach guarantees an efficient deletion of TMS-induced artifacts while preserving the integrity of EEG signals around TMS impulses. © 2008 IEEE
Leader-Follower Formation Control of Nonholonomic Mobile Robots with Input Constraints
No full textThe paper deals with leader–follower formations of nonholonomic mobile robots, introducing a formation control strategy alternative to those existing in the literature. Robots’ control inputs are forced to satisfy suitable constraints that restrict the set of leader possible paths and admissible positions of the follower with respect to the leader. A peculiar characteristic of the proposed strategy is that the follower position is not rigidly fixed with respect to the leader but varies in proper circle arcs centered in the leader reference frame
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