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Fast 3D Collision Avoidance Algorithm for Fixed Wing UAS

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Abstract

This paper presents an efficient 3D collision avoidance algorithm for fixed wing Unmanned Aerial Systems (UAS). The algorithm increases the ability of aircraft operations to complete mission goals by enabling fast collision avoidance of multiple obstacles. The new algorithm, which we have named Fast Geometric Avoidance algorithm (FGA), combines geometric avoidance of obstacles and selection of a critical avoidance start time based on kinematic considerations, collision likelihood, and navigation constraints. In comparison to a current way-point generation method, FGA showed a 90% of reduction in computational time for the same obstacle avoidance scenario. Using this algorithm, the UAS is able to avoid static and dynamic obstacles while still being able to recover its original trajectory after successful collision avoidance. Simulations for different mission scenarios show that this method is much more efficient at avoiding multiple obstacles than previous methods. Algorithm effectiveness validation is provided with Monte Carlo simulations and flight missions in an aircraft simulator. FGA was also tested on a fixed-wing aircraft with successful results. Because this algorithm does not have specific requirements on the sensor data types it can be applied to cooperative and non-cooperative intruders.

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References

  1. Xiang, H., Tian, L.: Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (uav). Biosyst. Eng. 108(2), 174–190 (2011)

    Article  MathSciNet  Google Scholar 

  2. Dalamagkidis, K., Valavanis, K.P., Piegl, L.A.: On unmanned aircraft systems issues, challenges and operational restrictions preventing integration into the national airspace system. Prog. Aerosp. Sci. 44(7–8), 503–519 (2008)

    Article  Google Scholar 

  3. Clarke, R., Moses, L.B.: The regulation of civilian drones’ impacts on public safety. Comput. Law Secur. Rev. 30(3), 263–285 (2014)

    Article  Google Scholar 

  4. Angelov, P.: Sense and Avoid in UAS: Research and Applications. Wiley, New York (2012)

    Book  Google Scholar 

  5. Karamouzas, I., Skinner, B., Guy, S.J.: Universal power law governing pedestrian interactions. Phys. Rev. Lett. 113(23), 238701 (2014)

    Article  Google Scholar 

  6. Linchant, J., Lisein, J., Semeki, J., Lejeune, P., Vermeulen, C.: Are unmanned aircraft systems (uas s) the future of wildlife monitoring? a review of accomplishments and challenges. Mammal Rev. 45(4), 239–252 (2015)

    Article  Google Scholar 

  7. Griffiths, S., Saunders, J., Curtis, A., Barber, B., McLain, T., Beard, R.: Obstacle and terrain avoidance for miniature aerial vehicles. In: Advances in Unmanned Aerial Vehicles, pp 213–244. Springer (2007)

  8. Kephart, R.J., Braasch, M.S.: See-and-avoid comparison of performance in manned and remotely piloted aircraft. IEEE Aerosp. Electron. Syst. Mag. 25(5), 36–42 (2010)

    Article  Google Scholar 

  9. von Essen, C., Giannakopoulou, D.: Analyzing the next generation airborne collision avoidance system. In: International Conference on Tools and Algorithms for the Construction and Analysis of Systems, pp 620–635. Springer (2014)

  10. Huerta, M.: Integration of civil unmanned aircraft systems (uas) in the national airspace system (nas) roadmap. Federal Aviation Administration, Retrieved Dec 19, 2013 (2013)

    Google Scholar 

  11. LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)

    Book  Google Scholar 

  12. Hoy, M., Matveev, A.S., Savkin, A.V.: Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey. Robotica 33(3), 463–497 (2015)

    Article  Google Scholar 

  13. Al-Mutib, K., AlSulaiman, M., Emaduddin, M., Ramdane, H., Mattar, E.: “D* lite based real-time multi-agent path planning in dynamic environments. In: 2011 Third International Conference on Computational Intelligence, Modelling & Simulation, pp 170–174. IEEE (2011)

  14. Souissi, O., Benatitallah, R., Duvivier, D., Artiba, A., Belanger, N., Feyzeau, P.: Path planning: a 2013 survey. In: Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM), pp 1–8. IEEE (2013)

  15. Lingelbach, F.: Path planning using probabilistic cell decomposition. IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004 1, 467–472 (2004)

    Article  Google Scholar 

  16. Mac, T.T., Copot, C., Tran, D.T., De Keyser, R.: Heuristic approaches in robot path planning: a survey. Robot. Auton. Syst. 86, 13–28 (2016)

    Article  Google Scholar 

  17. Raja, P., Pugazhenthi, S.: Optimal path planning of mobile robots: a review. International Journal of Physical Sciences 7(9), 1314–1320 (2012)

    Article  Google Scholar 

  18. Lozano-Pérez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM 22(10), 560–570 (1979)

    Article  Google Scholar 

  19. Sigurd, K., How, J.: Uav trajectory design using total field collision avoidance. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, p 5728 (2003)

  20. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Autonomous Robot Vehicles, pp 396–404. Springer (1986)

  21. Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: Proceedings. 1991 IEEE International Conference on Robotics and Automation, pp 1398–1404. IEEE (1991)

  22. Borenstein, J., Koren, Y.: Real-time obstacle avoidance for fast mobile robots. IEEE Trans. Syst. Man Cybern. 19(5), 1179–1187 (1989)

    Article  Google Scholar 

  23. Kazem, B.I., Hamad, A.H., Mozael, M.M.: Modified vector field histogram with a neural network learning model for mobile robot path planning and obstacle avoidance. Int. J. Adv. Comp. Techn. 2(5), 166–173 (2010)

    Google Scholar 

  24. Borenstein, J., Koren, Y.: The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans. Robot. Autom. 7(3), 278–288 (1991)

    Article  Google Scholar 

  25. Zhang, Y., Wang, G.: An improved rgb-d vfh+ obstacle avoidance algorithm with sensor blindness assumptions. In: 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE), pp 408–414. IEEE (2017)

  26. Bianco, G., Fiorini, P.: Visual avoidance of moving obstacles based on vector field disturbances. In: Proceedings 2001. ICRA IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), vol. 3, pp 2704–2709. IEEE (2001)

  27. Lin, Y., Saripalli, S.: Path planning using 3d dubins curve for unmanned aerial vehicles. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS), pp 296–304. IEEE (2014)

  28. Elbanhawi, M., Simic, M.: Sampling-based robot motion planning: a review. IEEE Access 2, 56–77 (2014)

    Article  Google Scholar 

  29. Pharpatara, P., Hérissé, B., Bestaoui, Y.: 3-D trajectory planning of aerial vehicles using rrt. IEEE Trans. Control Syst. Technol. 25(3), 1116–1123 (2017)

    Article  Google Scholar 

  30. Liu, P., Yu, H., Cang, S.: Optimized adaptive tracking control for an underactuated vibro-driven capsule system. Nonlinear Dyn 94(3), 1803–1817 (2018)

    Article  Google Scholar 

  31. Liu, P., Yu, H., Cang, S.: Trajectory synthesis and optimization of an underactuated microrobotic system with dynamic constraints and couplings. Int. J. Control. Autom. Syst. 16(5), 2373–2383 (2018)

    Article  Google Scholar 

  32. Yu, T., Tang, J., Bai, L., Lao, S.: Collision avoidance for cooperative uavs with rolling optimization algorithm based on predictive state space. Appl. Sci. 7(4), 329 (2017)

    Article  Google Scholar 

  33. Yang, P., Tang, K., Lozano, J.A., Cao, X.: Path planning for single unmanned aerial vehicle by separately evolving waypoints. IEEE Trans. Robot. 31(5), 1130–1146 (2015)

    Article  Google Scholar 

  34. Jang, D.-S., Chae, H.-J., Choi, H.-L: Optimal control-based uav path planning with dynamically-constrained tsp with neighborhoods. arXiv:612.06008 (2016)

  35. Bozhinoski, D., Bucchiarone, A., Malavolta, I., Marconi, A., Pelliccione, P.: Leveraging collective run-time adaptation for uav-based systems. In: 2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp 214–221. IEEE (2016)

  36. Jing-Lin, H., Xiu-Xia, S., Ri, L., Xiong-Feng, D., Mao-Long, L.: Uav real-time route planning based on multi-optimized rrt algorithm. In: 2017 29th Chinese Control and Decision Conference (CCDC), pp 837–842. IEEE (2017)

  37. Pascarella, D., Venticinque, S., Aversa, R: Autonomic agents for real time uav mission planning. In: 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC), pp 410–415. IEEE (2013)

  38. Mujumdar, A., Padhi, R.: Reactive collision avoidance of using nonlinear geometric and differential geometric guidance. J. Guid. Control Dynam. 34(1), 303–311 (2011)

    Article  Google Scholar 

  39. Minguez, J., Montano, L.: Nearness diagram (nd) navigation: collision avoidance in troublesome scenarios. IEEE Trans. Robot. Autom. 20(1), 45–59 (2004)

    Article  Google Scholar 

  40. Ge, S.S., Lai, X., Mamun, A.: Boundary following and globally convergent path planning using instant goals. IEEE Trans. Syst. Man Cybern. B Cybern. 35(2), 240–254 (2005)

    Article  Google Scholar 

  41. Kamon, I., Rivlin, E.: Sensory-based motion planning with global proofs. IEEE Trans. Robot. Autom. 13 (6), 814–822 (1997)

    Article  Google Scholar 

  42. Al-Mutib, K., Abdessemed, F.: Indoor mobile robot navigation in unknown environment using fuzzy logic based behaviors. Adv. Sci. Technol. Eng. Syst. J. 2, 327–337 (2017)

    Article  Google Scholar 

  43. Lumelsky, V.J., Stepanov, A.A.: Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape. Algorithmica 2(1-4), 403–430 (1987)

    Article  MathSciNet  Google Scholar 

  44. Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4(1), 23–33 (1997)

    Article  Google Scholar 

  45. Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–772 (1998)

    Article  Google Scholar 

  46. Guy, S.J., Van Den Berg, J., Lin, M.C., Manocha, D.: Geometric methods for multi-agent collision avoidance. In: Proceedings of the Twenty-Sixth Annual Symposium on Computational Geometry, pp 115–116. ACM (2010)

  47. Chakravarthy, A., Ghose, D.: Obstacle avoidance in a dynamic environment: a collision cone approach. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 28(5), 562–574 (1998)

    Article  Google Scholar 

  48. Pham, H., Smolka, S.A., Stoller, S.D., Phan, D., Yang, J.: A survey on unmanned aerial vehicle collision avoidance systems. arXiv:1508.07723 (2015)

  49. Seo, J., Kim, Y., Tsourdos, A.: Differential geometry based collision avoidance guidance for multiple uavs. IFAC Proceedings Volumes 46(19), 113–118 (2013)

    Article  Google Scholar 

  50. Haugen, J., Imsland, L.: Monitoring moving objects using aerial mobile sensors. IEEE Trans. Control Syst. Technol. 24(2), 475–486 (2016)

    Google Scholar 

  51. Luo, C., McClean, S.I., Parr, G., Teacy, L., De Nardi, R.: Uav position estimation and collision avoidance using the extended kalman filter. IEEE Trans. Veh. Technol. 62(6), 2749–2762 (2013)

    Article  Google Scholar 

  52. Saha, S., Natraj, A., Waharte, S.: A real-time monocular vision-based frontal obstacle detection and avoidance for low cost uavs in gps denied environment. In: 2014 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES), pp 189–195. IEEE (2014)

  53. Kwang, Y., Kwang, Y.: Performance simulation of radar sensor based obstacle detection and collision avoidance for smart uav. In: The 24th Digital Avionics Systems Conference, 2005. DASC 2005, vol. 2, p 10. IEEE (2005)

  54. Park, J.-W., Oh, H.-D., Tahk, M.-J.: Uav conflict detection and resolution based on geometric approach. Int. J. Aeronaut. Space Sci. 10(1), 37–45 (2009)

    Article  Google Scholar 

  55. Daniels, Z, Wright, L., Holt, J., Biaz, S.: Collision avoidance of multiple uas using a collision cone-based cost function. Computer Science and Software Engineering Department, Auburn University, Tech. Rep. CSSE12-07 (2012)

  56. Watanabe, Y., Calise, A., Johnson, E., Evers, J.: Minimum-effort guidance for vision-based collision avoidance. In: AIAA Atmospheric Flight Mechanics Conference and Exhibit, p 6641 (2006)

  57. Sasongko, R.A., Rawikara, S., Tampubolon, H.J.: Uav obstacle avoidance algorithm based on ellipsoid geometry. J. Intell. Robot. Syst. 88(2–4), 567–581 (2017)

    Article  Google Scholar 

  58. Yang, X., Alvarez, L.M., Bruggemann, T.: A 3d collision avoidance strategy for uavs in a non-cooperative environment. J. Intell. Robot. Syst. 70(1–4), 315–327 (2013)

    Article  Google Scholar 

  59. Carbone, C., Ciniglio, U., Corraro, F., Luongo, S.: A novel 3d geometric algorithm for aircraft autonomous collision avoidance. In: Proceedings of the 45th IEEE Conference on Decision and Control, pp 1580–1585. IEEE (2006)

  60. Bilimoria, K.: A geometric optimization approach to aircraft conflict resolution. In: 18th Applied Aerodynamics Conference, p 4265 (2000)

  61. Yoo, C., Cho, A., Park, B., Kang, Y., Shim, S., Lee, I.: Ads-b hils test for collision avoidance of smart uav. In: 2011 Tyrrhenian International Workshop on Digital Communications-Enhanced Surveillance of Aircraft and Vehicles, pp 253–257. IEEE (2011)

  62. Shin, H.-S., Tsourdos, A., White, B.: Uas conflict detection and resolution using differential geometry concepts. Sense and avoid in UAS: Research and Applications, vol. 62 (2012)

    Chapter  Google Scholar 

  63. Goss, J., Rajvanshi, R., Subbarao, K.: Aircraft conflict detection and resolution using mixed geometric and collision cone approaches. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, p 4879 (2004)

  64. Yang, D., Li, D., Sun, H.: 2D dubins path in environments with obstacle. Mathematical Problems in Engineering, vol. 2013 (2013)

  65. Lin, Z., Castano, L., Xu, H.: A fast obstacle collision avoidance algorithm for fixed wing uas. In: 2018 International Conference on Unmanned Aircraft Systems (ICUAS), pp 559–568. IEEE (2018)

  66. Wang, Q., Zhang, J.: Mpc and tgfc for uav real-time route planning. In: 2017 36th Chinese Control Conference (CCC), pp 6847–6850. IEEE (2017)

  67. Team, A.D.: Ardupilot autopilot suite. http://ardupilot.com/. Accessed, pp. 05–20 (2016)

  68. Meier, L., Camacho, J., Godbolt, B., Goppert, J., Heng, L., Lizarraga, M., et al.: Mavlink: Micro air vehicle communication protocol. Online]. Tillgänglig: http://qgroundcontrol.org/mavlink/start. [Hämtad 2014-05-22] (2013)

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Acknowledgments

The authors would like to thank the Maryland Industrial Partnerships (MIPS) Program and Millennium Engineering and Integration, Co. for their support of this project. We would also like to thank Patrick Fox, Henry Tuit Farquhar for implementation of autonomy hardware and for piloting the Apprentice aircraft. Thanks as well go to Camilo Melnyk who also implemented hardware on the Apprentice and for piloting the Phantom II quadcopter for videography as well as for management of the ground control station. Thanks to Sharon Shallom who also helped to manage the ground control station.

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  1. University of Maryland, College Park, MD, 20742, USA

    Zijie Lin, Lina Castano, Edward Mortimer & Huan Xu

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  1. Zijie Lin
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  2. Lina Castano
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Correspondence to Lina Castano.

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Lin, Z., Castano, L., Mortimer, E. et al. Fast 3D Collision Avoidance Algorithm for Fixed Wing UAS. J Intell Robot Syst 97, 577–604 (2020). https://doi.org/10.1007/s10846-019-01037-7

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