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A novel guidance and navigation system for MAVs capable of autonomous collision-free entering of buildings

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Abstract

Micro Aerial Vehicles for autonomous explorations of hazardous areas are predestined to support emergency and rescue forces. Especially the autonomous access to buildings is highly demanding due to insufficient GNSS reception in urban terrain and narrow passageways into buildings. Thus, this paper presents a complete flight system, consisting of guidance, navigation and control subsystems. All these elements are designed to enable safe flights into buildings. The guidance subsystem is divided into two parts. The vision based guidance part is manoeuvring the MAV on an intermediate position in front of the building. The potential field based guidance part enables the MAV to fly inside the buildings without having any collisions. For that, neither any prior knowledge about the building structure, nor any maps are necessary. To provide the flight guidance with information about the actual kinematic state of the MAV an accurate and robust navigation system not depending on GNSS measurements is used. The complete system is evaluated using simulated flight data.

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References

  1. Chen, X. and Zhang, J., The three-dimension path planning of UAV based on improved artificial potential field in dynamic environment, International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013, Bd. 2, pp. 144–147.

    Google Scholar 

  2. He, R., Prentice, S. und Roy, N.„ Planning in information space for a quadrotor helicopter in a GPS-denied environment,“ IEEE International Conference on Robotics and Automation (ICRA), 2008, pp. 1814–1820.

    Google Scholar 

  3. Nieuwenhuisen, M. und Behnke, S., Layered mission and path planning for MAV navigation with partial environment knowledge, in Proceedings of 13th International Conference on Intelligent Autonomous Systems (IAS), Padova, Italy, 2014.

    Google Scholar 

  4. Kim, S., Lee, D., and Kim, H. J., Image based visual servoing for an autonomous quadrotor with adaptive backstepping control, International Conference on Control, Automation and Systems (ICCAS), 2011, pp. 532–537.

    Google Scholar 

  5. Bills, C., Chen, J., and Saxena, A., Autonomous MAV flight in indoor environments using single image perspective cues,“ in IEEE International Conference on Robotics and Automation (ICRA), Shanghai, 2011.

    Google Scholar 

  6. Fahimi, F. and Thakur, K., An alternative closed-loop vision-based control approach for unmanned aircraft systems with application to a quadrotor, in International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, USA, 2013.

    Google Scholar 

  7. Hrabar, S. und Sukhatme, G., Vision-based navigation through urban canyons, Journal of Field Robotics, Bd. 26, Nr. 5, 2009, pp. 431–452.

    Article  Google Scholar 

  8. Zingg, S., Scaramuzza, D., Weiss, S., and Siegwart, R., MAV navigation through indoor corridors using optical flow, in IEEE International Conference on Robotics and Automation (ICRA), Anchorage, USA, 2010.

    Google Scholar 

  9. Nieuwenhuisen, M., Schadler, M. and Behnke, S., Predictive potential field-based collision avoidance for multicopters, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS), Bde. %1 von %2XL-1/W2, 2013, pp. 293–298.

    Google Scholar 

  10. Stastny, T., Garcia, G., and Keshmiri, S., Collision and obstacle avoidance in unmanned aerial systems using morphing potential field navigation and nonlinear model predictive control, Journal of Dynamic Systems, Measurement, and Control, Bd. 137, no. 1, 2014.

    Google Scholar 

  11. Crocoll, P., Seibold, J., Frietsch, N., and Trommer, G., Collision-free trajectory planning and local obstacle avoidance for MAVs, in Proceedings of the European Navigation Conference, London, United Kingdom, 2011.

    Google Scholar 

  12. Mejias, L., Saripalli, S., Campoy, P., and Sukhatme, G.S., Visual servoing of an autonomous helicopter in urban areas using feature tracking, Visual Servoing of an Autonoous Helicopter in Urban Areas using Feature Tracking, Bde. %1 von %23-4, no. 23, 2006, pp. 185–201.

    Google Scholar 

  13. Flores, G., Zhou, S., Lozano, R., and Castillo, P., A vision and GPS-based real-time trajectory planning for MAV in unknown urban environments, in International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, USA, 2013.

    Google Scholar 

  14. Julian, R.C., Rose, C.J., Hu, H., and Fearing, R.S., Cooperative control and modeling for narrow passage traversal with an ornithopter MAV and lightweight ground station, in Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Saint Paul, USA, 2013.

    Google Scholar 

  15. Popp, M., Crocoll, J., Ruppelt, J., and Trommer, G.F., A novel multi image based navigation system to aid outdoor–indoor transition flights of micro aerial vehicles, in Proceedings of ION GNSS+, Tampa, USA, 2014.

    Google Scholar 

  16. Suzuki, S. and Abe, K., Topological structural analysis of digitized binary images by border following, Computer Vision, Graphics, and Image Processing, Bd. 30, no. 1, 1985, pp. 32–46.

    Article  MATH  Google Scholar 

  17. Yaghobi, M., Jadaliha, M., Zolghadr, J., and Norouzi, M., Adaptive line extraction algorithm for SLAM application, in IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, 2009.

    Google Scholar 

  18. Bouguet, J., Pyramidal implementation of the affine Lucas Kanade feature tracker description of the algorithm, Intel Corporation, 2001.

    Google Scholar 

  19. Saurer, O., Fraundorfer, F., and Pollefeys, M., Homography based visual odometry with known vertical direction and weak Manhattan world assumption, in IEEE/IROS Workshop on Visual Control of Mobile Robots, 2012.

    Google Scholar 

  20. Hutchinson, S., Hager, G.D., and Corke, P.I. A Tutorial on visual servo control, IEEE Transactions on Robotics and Automation, Bd. 5, no. 12, 1996, pp. 651–670.

    Article  Google Scholar 

  21. Chaumette, F. and Hutchinson, S., Visual servo control, Part I: Basic approaches, IEEE Robotics and Automation Magazine, Bd. 13, 2006, pp. 82–90.

    Article  Google Scholar 

  22. Gans, N.R. und Hutchinson, S.A., An asymptotically stable switched system visual controller for eye in hand robots, in IEEE International Conference on Intelligent Robots and Systems, Las Vegas, USA, 2003.

    Google Scholar 

  23. V. 2. Documentation, Tutorial: How to boost your visual servo control, Lagadic project, 2014.

    Google Scholar 

  24. Chesi, G., Hashimoto, K., Prattichizzo, D., and Vicino, A., Keeping features in the field of view in eyein-hand visual servoing: a switching approach, IEEE Transactions on Robotics, 2004, Bd. 20, no. 5, p. 908–914, 2004.

    Article  Google Scholar 

  25. Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots, IEEE International Conference on Robotics and Automation, 1985, Bd. 2, pp. 500–505.

    Google Scholar 

  26. Koren, Y. and Borenstein, J., potential field methods and their inherent limitations for mobile robot navigation, IEEE International Conference on Robotics and Automation, 1991, Bd. 2, pp. 1398–1404.

    Google Scholar 

  27. Meister, O., Entwurf und Realisierung einer Aufklärungsplattform auf Basis eines unbemannten Minihelikopters mit autonomen Flugfähigkeiten, Karlsruhe, Germany: Institute of Systems Optimization, Karlsruhe Institute of Technology (KIT), 2010.

    Google Scholar 

  28. Chengqing, L., Ang, V.M.H., Krishnan, H., and Lim, S.Y., Virtual obstacle concept for local-minimum-Rrcovery in potential-field based navigation, Proceedings of the IEEE International Conference on Robotics and Automation, 2000, Bd. 2, no. 2, pp. 983–988.

    Google Scholar 

  29. Ge, S.S. and Cui, Y.J., New potential functions for mobile robot path planning, IEEE Transactions on Robotics and Automation, 2000, Bd. 16, no. 5, pp. 615–620.

    Article  Google Scholar 

  30. Koditschek, D.E., Exact robot navigation by means of potential functions: Some topological considerations, IEEE International Conference on Robotics and Automation, 1987, Bd. 4, pp. 1–6.

    Google Scholar 

  31. Volpe, R. and Khosla, P., Manipulator control with superquadric artificial potential functions: Theory and experiments, IEEE Transactions on Systems, Man and Cybernetics, 1990, Bd. 20, no. 6, p. 1423–1436.

    Article  Google Scholar 

  32. Rezaee, H., and Abdollahi, F., Adaptive artificial potential field approach for obstacle avoidance of unmanned aircrafts, Proceedings of the European Navigation Conference, 2012, pp. 1–6.

    Google Scholar 

  33. Hwang, Y.K. and Ahuja, N., A potential field approach to path planning, IEEE Transactions on Robotics and Automation, 1992, Bd. 8, Nr. 1, pp. 23–32.

    Article  Google Scholar 

  34. Gerlach, A.R., Kingston, D., and Walker, B.K., UAV navigation using predictive vector field control, in American Control Conference (ACC), Portland, USA, 2014.

    Google Scholar 

  35. Frietsch, N., Bildbasiertes Navigationssystem eines unbemannten Mini-Helikopters, Berlin: Logos Verlag GmbH, 2012.

    Google Scholar 

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Correspondence to M. Popp.

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Published in Giroskopiya i Navigatsiya, 2015, No. 2, pp. 3–17.

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Popp, M., Prophet, S., Scholz, G. et al. A novel guidance and navigation system for MAVs capable of autonomous collision-free entering of buildings. Gyroscopy Navig. 6, 157–165 (2015). https://doi.org/10.1134/S2075108715030128

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  • DOI: https://doi.org/10.1134/S2075108715030128

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