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


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.

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Available from: Jim Zhu, Sep 03, 2014
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    • "In this section, the sensor fusion method for our omnidirectional mobile robots is briefly described. Detailed design and test results are published in [33]. The sensor fusion method combines on-board encoder sensor and the global vision system measurements, thereby providing reliable and accurate position and orientation measurements. "
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    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.
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