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Sliding mode collision-free navigation for quadrotors using monocular vision

Published online by Cambridge University Press:  20 June 2018

Diego Mercado ,
Pedro Castillo  and
Rogelio Lozano
Diego Mercado*
Affiliation:
Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854-8058, USA
Pedro Castillo
Affiliation:
Sorbonne Universitès, Université de Technologie de Compiègne, CNRS, Heudiasyc UMR 7253, CS 60 319, 60 203 Compiègne cedex, France. E-mail: [email protected]
Rogelio Lozano
Affiliation:
Sorbonne Universitès, Université de Technologie de Compiègne, CNRS, Heudiasyc UMR 7253, CS 60 319, 60 203 Compiègne cedex, France. E-mail: [email protected] LAFMIA UMI 3175 CINVESTAV-CNRS, Avenida Instituto Politècnico Nacional 2508, San Pedro Zacatenco, 07360 Mexico City, CDMX, Mexico. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Safe and accurate navigation for autonomous trajectory tracking of quadrotors using monocular vision is addressed in this paper. A second order Sliding Mode (2-SM) control algorithm is used to track desired trajectories, providing robustness against model uncertainties and external perturbations. The time-scale separation of the translational and rotational dynamics allows to design position controllers by giving a desired reference in roll and pitch angles, which is suitable for practical validation in quad-rotors equipped with an internal attitude controller. A Lyapunov based analysis proved the closed-loop stability of the system despite the presence of unknown external perturbations. Monocular vision fused with inertial measurements are used to estimate the vehicle's pose with respect to unstructured scenes. In addition, the distance to potential collisions is detected and computed using the sparse depth map coming also from the vision algorithm. The proposed strategy is successfully tested in real-time experiments, using a low-cost commercial quadrotor.

Keywords

UAVsQuadrotorsAutonomous navigationSliding modeCollision avoidanceComputer vision
Type
Articles
Information
Robotica , Volume 36 , Issue 10 , October 2018 , pp. 1493 - 1509
Copyright
Copyright © Cambridge University Press 2018 

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