Autonomous quadrotor collision avoidance and destination seeking in a GPS-denied environment
- PDF / 4,158,564 Bytes
- 20 Pages / 595.276 x 790.866 pts Page_size
- 43 Downloads / 201 Views
Autonomous quadrotor collision avoidance and destination seeking in a GPS-denied environment Thomas Kirven1 · Jesse B. Hoagg1 Received: 14 February 2020 / Accepted: 22 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract We present a new integrated guidance and control method for autonomous collision avoidance and navigation in an unmapped GPS-denied environment that contains unknown obstacles. The algorithm is implemented on an experimental custom quadrotor that uses onboard vision sensing (i.e., an Intel RealSense R200) to detect the positions of obstacles. We demonstrate autonomous collision avoidance and destination seeking in experiments, where the quadrotor navigates unknown GPS-denied environments. All feedback measurements are obtained from onboard sensors. The new guidance and control algorithm uses a nonlinear inner-loop attitude controller; a nonlinear middle-loop velocity controller; and an ellipsoidal-potential-field outerloop guidance algorithm for collision avoidance and destination seeking. The main analytic result regarding the inner-loop control shows that every quadrotor attitude with pitch between ±90◦ is a locally exponentially stable equilibrium of the closed-loop attitude dynamics, and we quantify the region of attraction for each attitude equilibrium. Keywords Quadcopter · Collision avoidance · Vision sensing
1 Introduction Improvements in sensing and computing have increased the capabilities of unmanned aerial vehicles (UAVs). Quadrotors, in particular, have gained popularity because of their maneuverability and simple mechanical design. Quadrotors are capable of vertical take off and landing, hovering, pivoting about a fixed point, and instantaneous translational acceleration in three dimensions (Hoffmann et al. 2007). These capabilities make quadrotors well suited for many aerial-robotic applications. Traditionally, guidance and navigation for quadrotors are handled by a human pilot, who operates the vehicle using This work is supported in part by the National Science Foundation (OIA-1539070). Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10514-020-09949-2) contains supplementary material, which is available to authorized users.
B
Jesse B. Hoagg [email protected] Thomas Kirven [email protected]
1
Department of Mechanical Engineering, University of Kentucky, Lexington, KY 40506-0503, USA
line of sight or video from an onboard camera. However, computer and sensor advances (e.g., miniaturization, reduced cost) have made autonomous flight for quadrotors feasible (e.g., Bachrach et al. 2012, 2011). Autonomous quadrotors could enable applications such as autonomous transport, autonomous distributed sensing (Lippay and Hoagg 2019, 2020; Punzo et al. 2014; Saska et al. 2016), cooperative formation flying (Murray 2007; Turpin et al. 2012, 2014; Wellman and Hoagg 2017a, b, 2018), and cooperative construction (Auguliaro et al. 2014; Lindsey et al. 2012). Quadrotor dynamics include a translational
Data Loading...
