Abstract
The use of quadrotors to civilian and military missions has been increased and the challenges involved on controlling it, mainly in indoors and restricted environments, has been attracting robotics researchers. Thus, an automatic collision avoidance approach is of utmost importance in this scenario, given the difficulty of control and the risk of accidents involved in the use of these vehicles. This paper presents an approach for obstacle avoidance of a manually controlled quadrotor automatically, allowing the operator to keep focus on the overall mission. The method is based on constantly estimating its future path considering its dynamics, current status, current control and distances measured by four on-board sonar sensors. Simultaneously, the pose is estimated based on the quadrotor odometry and an occupation grid representation of the nearby environment is constructed using the sonar sensors measurements. All that information is used to determine an imminent collision and overrides the user control, if necessary, keeping its last safe position. All the solution was evaluated in a simulator, the real quadrotor’s and sonars sensors were characterized to be embedded in the quadrotor through a computer-on-module and controlled over wireless network communication.
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References
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)
Ross, S., Melik-Barkhudarov, N., Shankar, K.S., Wendel, A., Dey, D., Bagnell, J.A., Hebert, M.: Learning monocular reactive UAV control in cluttered natural environments. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 1765–1772. IEEE (2013)
Lugo, J.J., Zell, A.: Framework for autonomous on-board navigation with the AR.Drone. J. Intell. Robot. Syst. 73(1–4), 401–412 (2014)
Zhang, X., Li, X., Wang, K., Lu, Y.: A survey of modelling and identification of quadrotor robot. In: Abstract and Applied Analysis, vol. 2014. Hindawi Publishing Corporation (2014)
Salih, A.L., Moghavvemi, M., Mohamed, H.A., Gaeid, K.S.: Flight PID controller design for a UAV quadrotor. Sci. Res. Essays 5(23), 3660–3667 (2010)
Nicol, C., Macnab, C., Ramirez-Serrano, A.: Robust neural network control of a quadrotor helicopter. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering, 1233–1237 (2008)
Ito, K., Han, J., Ohya, A.: Localization using uniaxial laser rangefinder and IMU for MAV. In: 2014 IEEE/SICE International Symposium on System Integration (SII), pp. 712–717. IEEE (2014)
Shen, S., Mulgaonkar, Y., Michael, N., Kumar, V.: Vision-based state estimation for autonomous rotorcraft MAVs in complex environments. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 1758–1764. IEEE (2013)
Mendes, J., Ventura, R.: Assisted teleoperation of quadcopters using obstacle avoidance. J. Autom. Mobile Robot. Intell. Syst. 7(1), 54–58 (2013)
Grzonka, S., Grisetti, G., Burgard, W.: A fully autonomous indoor quadrotor. IEEE Trans. Robot. 28(1), 90–100 (2012)
Kerns, A.J., Shepard, D.P., Bhatti, J.A., Humphreys, T.E.: Unmanned aircraft capture and control via GPS spoofing. J. Field Robot. 31(4), 617–636 (2014)
Roberts, A., Tayebi, A.: A new position regulation strategy for VTOL UAVs using IMU and GPS measurements. Automatica 49(2), 434–440 (2013)
Israelsen, J., Beall, M., Bareiss, D., Stuart, D., Keeney, E., van den Berg, J.: Automatic collision avoidance for manually tele-operated unmanned aerial vehicles. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 6638–6643 (2014)
Becker, M., Sampaio, R.C.B., Bouabdallah, S.: Perrot, Vincent de Siegwart, R.: In-flight collision avoidance controller based only on OS4 embedded sensors. J. Braz. Soc. Mech. Sci. Eng. 34(3), 294–307 (2012)
Gupte, S., Mohandas, P.I.T., Conrad, J.M.: A survey of quadrotor unmanned aerial vehicles. In: 2012 Proceedings of IEEE SoutheastCon, pp. 1–6. IEEE (2012)
Mian, A.A., Daobo, W.: Modeling and backstepping-based nonlinear control strategy for a 6 DOF quadrotor helicopter. Chin. J. Aeronaut. 21(3), 261–268 (2008)
Dijkshoorn, N., Visser, A.: Integrating sensor and motion models to localize an autonomous AR.Drone. Int. J. Micro Air Veh. 3(4), 183–200 (2011)
Beard, R.W., McLain, T.W.: Small Unmanned Aircraft: Theory and Practice. Princeton University Press, Princeton (2012)
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34(3), 189–206 (2013)
Dawson, R.: How significant is a boxplot outlier. J. Stat. Educ. 19(2), 1–12 (2011)
Acknowledgements
This research is partially funded by CAPES (Brazilian Research Agency) through Edital Pro-Estrategia \(N^o\) 050/2011 and Demanda Social and Petrobras SA.
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Giovanini, B., Oliveira, H.A., Rosa, P.F.F. (2017). An Automatic Collision Avoidance Approach to Assist Remotely Operated Quadrotors. In: Chen, W., Hosoda, K., Menegatti, E., Shimizu, M., Wang, H. (eds) Intelligent Autonomous Systems 14. IAS 2016. Advances in Intelligent Systems and Computing, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-319-48036-7_16
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DOI: https://doi.org/10.1007/978-3-319-48036-7_16
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