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On-board model-based automatic collision avoidance: application in remotely-piloted unmanned aerial vehicles

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Abstract

This paper focuses on real-world implementation and verification of a local, model-based stochastic automatic collision avoidance algorithm, with application in remotely-piloted (tele-operated) unmanned aerial vehicles (UAVs). Automatic collision detection and avoidance for tele-operated UAVs can reduce the workload of pilots to allow them to focus on the task at hand, such as searching for victims in a search and rescue scenario following a natural disaster. The proposed algorithm takes the pilot’s input and exploits the robot’s dynamics to predict the robot’s trajectory for determining whether a collision will occur. Using on-board sensors for obstacle detection, if a collision is imminent, the algorithm modifies the pilot’s input to avoid the collision while attempting to maintain the pilot’s intent. The algorithm is implemented using a low-cost on-board computer, flight-control system, and a two-dimensional laser illuminated detection and ranging sensor for obstacle detection along the trajectory of the robot. The sensor data is processed using a split-and-merge segmentation algorithm and an approximate Minkowski difference. Results from flight tests demonstrate the algorithm’s capabilities for tele-operated collision-free control of an experimental UAV.

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References

  • Abdilla, A., Richards, A., & Burrow, S. (2015). Power and endurance modelling of battery-powered rotorcraft. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 675– 680).

  • Achtelik, M. W., Lynen, S., Weiss, S., Chli, M., & Siegwart, R. (2014). Motion- and uncertainty-aware path planning for micro aerial vehicles. Journal of Field Robotics, 31(4), 676–698.

    Article  Google Scholar 

  • Adams, M., Wijesoma, W. S., & Shacklock, A. (2007). Autonomous navigation: Achievements in complex environments. IEEE Instrumentation and Measurement, 10(3), 15–21.

    Article  Google Scholar 

  • Agrawal, P., Ratnoo, A., & Ghose, D. (2015). Vision based obstacle detection and avoidance for UAVs using image segmentation. In AIAA guidance, navigation, and control conference (pp. 848–857).

  • Astilla, O., Guerrero, J., Mendoz, R., Teriz, P., & Roxas, M .(2015). Obstacle avoidance of hybrid mobile-quadrotor vehicle with range sensors using fuzzy logic control. In International conference on humanoid, nanotechnology, information technology, communication and control, environment and management (pp. 1–8).

  • Bareiss, D., van den Berg J, & Leang, K. K. (2015). Stochastic automatic collision avoidance for tele-operated unmanned aerial vehicles. In: IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4818–4825).

  • Barrientos, A., Colorado, J., Cerro, J. D., Martinez, A., Rossi, C., Sanz, D., et al. (2011). Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots. Journal of Field Robotics, 28(5), 667–689.

    Article  Google Scholar 

  • Behar, E., & Lien, J. M. (2011). Fast and robust 2d minkowski sum using reduced convolution. In: IEEE/RSJ international conference on intelligent robots and systems (pp. 1573–1578).

  • Bernini, N., Bertozzi, M., Castangia, L., Patander, M., & Sabbatelli, M. (2014). Real-time obstacle detection using stereo vision for autonomous ground vehicles: a survey. In IEEE Internationl conference on intelligent Transportation Systmes (pp. 873–878).

  • Borenstein, J., & Koren, Y. (1991). The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Transactions on Robotics and Automation, 7(3), 278–288.

    Article  Google Scholar 

  • Brand, C., Schuster, M. J., Hirschmuller, H., & Suppa, M. (2014). Stereo-vision based obstacle mapping for indoor/outdoor SLAM. In: IEEE/RSJ International conference on intelligent robots and systems (pp. 1846–1853).

  • Brown, T., Doshi, S., Jadhav, S., & Himmelstain, J. (2004). Test bed for a wireless network on small UAVs. In: AIAA 3rd ”Unmanned Unlimited” technical conference, workshop, and exhibit (pp. 20–23).

  • Chryssanthacopoulos, J. P., & Kochenderfer, M. J. (2011). Accounting for state uncertainty in collision avoidance. Journal of Guidance, Control, and Dynamics, 34(4), 951–960.

    Article  Google Scholar 

  • Cole, D. T., Sukkarieh, S., & Göktoğan, A. H. (2006). System development and demonstration of a uav control architecture for information gathering missions. Journal of Field Robotics, 23(6–7), 417–440.

    Article  Google Scholar 

  • Cook, Z., Zhao, L., Lee, J., & Yim, W. (2015). Unmanned aerial system for first responders. In: 12th international conference on ubiquitous robots and ambient intelligence (URAI) (pp. 306–310).

  • D’Attanasio, S., Tonet, O., Megali, G., Carrozza, M. C., & Dario, P. (2000). A semi-automatic handheld mechatronic endoscope with collision-avoidance capabilities. In: IEEE international conference on robotics and automation (pp. 1586–1591).

  • De Berg, M., Cheong, O., van Kreveld, M., & Overmars, M. (2008). Computational Geometry: Algorithms and Applications. Berlin: Springer.

    Book  MATH  Google Scholar 

  • Fiorini, P., & Shiller, Z. (1998). Motion planning in dynamic environmnts using velocity obstacles. International Journal of Robotics Research, 17(7), 760–772.

    Article  Google Scholar 

  • Fox, D., Burgard, W., & Thrun, S. (1997). The dynamic window approach to collision avoidance. IEEE Robotics and Automation Magazine, 4(1), 23–33.

    Article  Google Scholar 

  • Gatti, M., Giulietti, F., & Turci, M. (2015). Maximum endurance for battery-powered rotary-wing aircraft. Aerospace Science and Technology, 45, 174–179.

    Article  Google Scholar 

  • Goodrich, M. A., Morse, B. S., Gerhardt, D., Cooper, J. L., Quigley, M., Adams, J. A., et al. (2008). Supporting wilderness search and rescue using a camera equipped mini UAV. Journal of Field Robotics, 25(1–2), 89–110.

    Article  Google Scholar 

  • Han, J., Xu, Y., Di, L., & Chen, Y. (2013). Low-cost multi-UAV technologies for contour mapping of nuclear radiation field. Journal of Intelligent & Robotic Systems, 70(1), 401–410.

    Article  Google Scholar 

  • Hausamann, D., Zirnig, W., Schreier, G., & Strobl, P. (2005). Monitoring of gas pipelines a civil UAV application. Aircraft Engineering and Aerospace Technology, 77(5), 352–360.

    Article  Google Scholar 

  • Hooi, C. G., Lagor, F. D., & Paley, D. A. (2015). Flow sensing, estimation and control for rotorcraft in ground effect. In Proceedings of the IEEE aerospace conference (pp. 1 – 8).

  • Huh, K., Park, J., Hwang, J., & Hong, D. (2008). A stereo vision-based obstacle detection system in vehicles. Optics and Lasers in engineering, 26(2), 168–178.

    Article  Google Scholar 

  • Israelsen, J., Beall, M., Bareiss, D., Stuart, D., Keeney, E., & van den Berg, J. (2014). Automatic collision avoidance for manually tele-operated unmanned aerial vehicles. In: IEEE international conference on robotics and automation (pp. 6638–6643).

  • Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, 5(1), 90–98.

    Article  Google Scholar 

  • Kumar, M., Cohen, K., & Homchaudhuri, B. (2011). Cooperative control of multiple uninhabited aerial vehicles for monitoring and fighting wildfires. Journal of Aerospace Computing, Information, and Communication, 8(1), 1–16.

    Article  Google Scholar 

  • Landis, G. A. (2004). Robots and humans: Synergy in planetary exploration. Acta astronautica, 55(12), 985–990.

    Article  Google Scholar 

  • Lien, J. M. (2007). Point-based minkowski sum boundary. In 15th Pacific conference on computer graphics and applications (pp. 261–270).

  • Lin, P. S., Hagen, L., Valavanis, K., & Zhou, H. (2005). Vision of unmanned aerial vehicle (UAV) based traffic management for incidents and emergencies. In 12th World congress on intelligent transport systems (pp. 1–12).

  • Li, S., & Tao, G. (2009). Feedback based adaptive compensation of control system sensor uncertainties. Automatica, 45(2), 393–404.

    Article  MathSciNet  MATH  Google Scholar 

  • Mahony, R., Kumar, V., & Corke, P. (2012). Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor. IEEE Robotics & Automation Magazine, 19(3), 20–32.

    Article  Google Scholar 

  • Maier, D., Hornung, A., & Bennewitz, M. (2012). Real-time navigation in 3d environments based on depth camera data. In International conference on humanoid robots (pp. 692–697).

  • Matthies, L., Brockers, R., Kuwata, Y., & Weiss, S. (2014). Stereo vision-based obstacle avoidance for micro air vehicles using disparity space. In IEEE international conference on robotics and automation (pp. 3242–3249).

  • Mejias, L., McNamara, S., Lai, J., & Ford, J. (2010). Vision-based detection and tracking of aerial targets for UAV collision avoidance. In: IEEE/RSJ International conference on intelligent robots and systems (pp. 87–92).

  • Mendes, J., & Ventura, R. (2013). Assisted teleoperation of quadcopters using obstacle avoidance. Journal of Automation, Mobile Robotics, & Intelligent Systems, 7(1), 54–58.

    Google Scholar 

  • Mohammed, F., Idries, A., Mohamed, N., Al-Jaroodi, J., & Jawhar, I. (2014). UAVs for smart cities: Opportunities and challenges. In International conference on unmanned aircraft systems (ICUAS) (pp. 267 – 273).

  • Montemerlo, M., Thrun, S., Koller, D., & Wegbreit, B. (2002). FastSLAM: A factored solution to the simultaneous localization and mapping problem. In AAAI-02 proceedings (pp. 593–598).

  • Muller, J., & Sukhatme, G. S. (2014). Risk-aware trajectory generation with application to safe quadrotor landing. In: IEEE/RSJ international conference on intelligent robots and systems (pp. 3642–3648).

  • Neri, M., Campi, A., Suffritti, R., Grimaccia, F., Sinogas, P., & Guye, O et al. (2011). SkyMedia-UAV-based capturing of HD/3D content with WSN augmentation for immersive media experiences. In: IEEE international conference on multimedia and expo (ICME) (pp. 1–6). doi:10.1109/ICME.2011.6012133

  • Nex, F., & Remondino, F. (2013). UAV for 3D mapping applications: A review. Applied Geomatics, 6(1), 1–15.

    Article  Google Scholar 

  • Nguyen, V., Gachter, S., Martinelli, A., Tomatis, N., & Siegwart, R. (2007). A comparison of line extraction algorithms using 2d range data for indoor mobile robotics. Autonomous Robots, 23(2), 97–111.

    Article  Google Scholar 

  • Niewenhuisen, M., & Behnke, S. (2015). 3d planning and trajectory optimization for real-time generation of smooth MAV trajectories. In: European conference on mobile robots (pp. 1–7).

  • Patil, S., van den Berg, J., & Alterovitz, R. (2012). Estimating probability of collision for safe planning under gaussian motion and sensing uncertainty. In: IEEE international conference on robotics and automation (pp. 3238–3244).

  • Rehmtullah, F., & Kelly, J. (2015). Vision-based collision avoidance for personal aerial vehicles using dynamic potential fields. In: 12th conference on computer and robot vision (pp. 297–304).

  • Rodriguez-Seda, E. J., Stipanovic, D. M., & Spong, M. W. (2011). Collision avoidance with sensing uncertainties. In American control conference (pp. 3363–3368).

  • Saha, S., Natraj, A., & Waharte, S. (2014). A real-time monocular vision-based frontal obstacle detection and avoidance for low cost UAVs in GPS denied environment. In: IEEE International conference on aerospace electronics and remote sensing technology (pp. 189–195).

  • Stegagno, P., Basile, M., Bulthoff, H. H., & Franchi, A. (2014). A semi-autonomous UAV platform for indoor remote operation with visual and haptic feedback. In IEEE International conference on robotics and automation (pp. 3862–3869).

  • Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., et al. (2012). Toward a fully autonomous UAV: Research platform for indoor and outdoor urban search and rescue. IEEE Robotics and Automation Magazine, 19(3), 46–56.

    Article  Google Scholar 

  • Trammell, H. S., Perry, A. R., Kumar, S., Czipott, P. V., Whitecotton, B. R., & McManus, T. J., et al. (2005). Using unmanned aerial vehicle-borne magnetic sensors to detect and locate improvised explosive devices and unexploded ordnance. In Proceedings of the SPIE sensors, and command, control, communications, and intelligence (C3I) technologies for homeland security and homeland defense IV, vol. 5778.

  • Valavanis, K. P., & Vachtsevanos, G. J. (2014). UAV Sense, Detect and Avoid: Introduction. Netherlands: Springer.

    Google Scholar 

  • van den Berg, J., Guy, S. J., Lin, M., & Manocha, D. (2011). Reciprocal n-body collision avoidance. In Proceedings of the international symposium on robotics research (pp. 3–19).

  • Waharte, S., & Trigoni, N. (2010). Supporting search and rescue operations with UAVs. In IEEE International Conference on Emerging Security Technologies (EST).

  • Wang, T., Bu, L., & Huang, Z. (2015). A new method for obstacle detection based on kinect depth image. In: Chinese automation congress(pp. 537–541).

  • Wang, W. P. (1990). Three-dimensional collision avoidance in production automation. Computers in Industry, 15(3), 169–174.

    Article  MathSciNet  Google Scholar 

  • Yeo, D. W., Sydney, N., & Paley, D. A. (2016). Onboard flow sensing for rotary-wing uav pitch control in wind. In: AIAA guidance, navigation, and control conference (pp. 1386–1396).

  • Yoshimoto, H., Jo, K., & Hori, K. (2009). Toward entertainment blimps for everyone by everyone. In Proceedings of the seventh ACM conference on creativity and cognition (pp. 445–446).

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Acknowledgements

This material is based upon work supported by the National Science Foundation, Partnership for Innovation Program, Grant No. 1430328. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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  1. Design, Automation, Robotics, and Control (DARC) Laboratory, University of Utah Robotics Center, Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA

    Daman Bareiss, Joseph R. Bourne & Kam K. Leang

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  1. Daman Bareiss
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  2. Joseph R. Bourne
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  3. Kam K. Leang
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Correspondence to Kam K. Leang.

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Bareiss, D., Bourne, J.R. & Leang, K.K. On-board model-based automatic collision avoidance: application in remotely-piloted unmanned aerial vehicles. Auton Robot 41, 1539–1554 (2017). https://doi.org/10.1007/s10514-017-9614-4

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