Conference Paper

Integrated Control and Navigation for Omni-directional Mobile Robot Based on Trajectory Linearization

Ohio Univ., Athens
DOI: 10.1109/ACC.2007.4282967 Conference: American Control Conference, 2007. ACC '07
Source: IEEE Xplore

ABSTRACT In this paper, an integrated navigation and control for omni-directional mobile robot is developed. Both control and navigation algorithms are based on trajectory linearization. The robot control is based on trajectory linearization control (TLC), in which an open-loop kinematic inversion and a closed-loop linear time varying (LTV) stabilizer are combined together to provide robust and accurate trajectory tracking performance. The LTV stabilizer is designed along the nominal trajectory provided by the kinematic inversion. The robot navigation is based on a sensor fusion using nonlinear Kalman filter which is also designed along the nominal trajectory. The sensor fusion combines onboard sensor and vision system measurements together, and provides reliable and accurate location and orientation measurements. Gating technology is employed to remove the inaccurate vision measurement. A real-time hardware-in-the- loop (HIL) simulation system was built to verified the proposed integrated control and navigation. Test results show that the proposed method improves robot location and orientation measurements reliability and accuracy, thus it improves the robot controller performance significantly.

  • Source
    Robot Vision, 03/2010; , ISBN: 978-953-307-077-3
  • [Show abstract] [Hide abstract]
    ABSTRACT: Most haptic devices share two main limits: they are grounded and they have limited workspace. A possible solution is to create haptic interfaces by combining mobile robots and standard grounded force-feedback devices, the so called Mobile Haptic Interfaces (MHIs). However, MHIs are characterized by dynamical limitations due to performance of the employed devices. This paper focuses on basic design issues and presents a novel (prototype) Mobile Haptics Platform that employs the coordination of numerically controlled wheel torques to render forces to a user handle placed on the top of the device. The interface, consisting in a small omni-directional robot, is link-less, fully portable and it has been designed to support home-rehabilitation exercises. In the present paper we shall review relevant choices concerning the functional aspects and the control design. In particular a specific embedded sensor fusion was implemented to allow the device to move on a desk without drifting. The sensor fusion algorithm has been optimized to provide users with a quality force feedback while ensuring accurate position tracking. The two requirements are in contrast each other and a specific variant of the Extended Kalman Filter (EKF) was required to allow the device working.
    World Haptics Conference (WHC), 2011 IEEE; 07/2011
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a nonlinear controller design for an omni-directional mobile robot is presented. The robot controller consists of an outer-loop (kinematics) controller and an inner-loop (dynamics) controller, which are both designed using the Trajectory Linearization Control (TLC) method based on a nonlinear robot dynamic model. The TLC controller design combines a nonlinear dynamic inversion and a linear time-varying regulator in a novel way, thereby achieving robust stability and performance along the trajectory without interpolating controller gains. A sensor fusion method, which combines the onboard sensor and the vision system data, is employed to provide accurate and reliable robot position and orientation measurements, thereby reducing the wheel slippage induced tracking error. A time-varying command filter is employed to reshape an abrupt command trajectory for control saturation avoidance. The real-time hardware-in-the-loop (HIL) test results show that with a set of fixed controller design parameters, the TLC robot controller is able to follow a large class of 3-degrees-of-freedom (3DOF) trajectory commands accurately.
    Robotics and Autonomous Systems. 01/2008;

Full-text (2 Sources)

Available from
Sep 3, 2014