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On Aerial Indoor Position Control and System Integration for Quadcopters Using Lidars and Inertial Measurement Units

[+] Author Affiliations
Nathan Zimmerman, Kellen Carey, Cristinel Ababei

Marquette University, Milwaukee, WI

Paper No. DSCC2016-9842, pp. V001T05A003; 10 pages
doi:10.1115/DSCC2016-9842
From:
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME

abstract

The main contribution of this paper is to introduce a computationally efficient iterative closest line (ICL) algorithm for determining indoor position drift of a quadcopter using minimal lidar data. In addition, we present the system-level design and implementation of a new quadcopter both as hardware and flight control algorithms. Such a platform allows us to develop and experiment new control and system optimization techniques. As an example, we discuss how the proposed ICL algorithm is used for position hold and control purposes by plugging it into the low level implementation of the flight control algorithm of the quadcopter. For testing and validation we use simulations with real world data. As part of the system-level design aspects, we present an investigation of the quadcopter power consumption. We are interested in power consumption because it is the major factor that determines the flight time of a typical quadcopter. We believe that this work is a contribution toward achieving better quadcopter design and control for indoor autonomous navigation.

Copyright © 2016 by ASME

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