Skip to main content
Log in

Geofence Boundary Violation Detection in 3D Using Triangle Weight Characterization with Adjacency

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper introduces a computationally efficient geofence boundary violation detection method using the Triangle Weight Characterization with Adjacency (TWCA) algorithm. The geofence is defined as a maximum and a minimum altitude, and a horizontal boundary specified as a polygon that does not self-intersect. TWCA initialization divides the horizontal component and bounding box of each geofence into a finite set of triangles, then determines the triangle containing the vehicle. During flight, each position update is checked for containment within the vertical geofence boundaries using inequalities and the horizontal geofence boundaries using TWCA. TWCA searches for the triangle containing the vehicle position using breadth-first search of the adjacency graph. The root node of the search is the triangle occupied at the previous time step. This algorithm is applicable to three-dimensional geofences containing both keep-in (inclusion) geofences and keep-out (exclusion) geofences.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Alciatore, D., Miranda, R.: A winding number and point-in-polygon algorithm. Glaxo Virtual Anatomy Project Research Report, Department of Mechanical Engineering, Colorado State University (1995)

  2. Garey, M.R., Johnson, D.S., Preparata, F.P., Tarjan, R.E.: Triangulating a simple polygon. Inf. Process. Lett. 7(4), 175–179 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  3. Gilabert, R.V., Dill, E.T., Hayhurst, K.J., Young, S.D.: Safeguard: Progress and test results for a reliable independent on-board safety net for UAS. In: 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), pp. 1–9. IEEE (2017)

  4. Government US: Operation and certification of small unmanned aircraft systems. In: Title 14 Code of Federal Regulations, Part 107 (2016)

  5. Hormann, K., Agathos, A.: The point in polygon problem for arbitrary polygons. Comput. Geom. 20(3), 131–144 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  6. Huang, C.W., Shih, T.Y.: On the complexity of point-in-polygon algorithms. Comput. Geosci. 23(1), 109–118 (1997)

    Article  Google Scholar 

  7. Kopardekar, P.H.: Unmanned aircraft systems traffic management (UTM) safely enabling UAS operations in low-altitude airspace (2017)

  8. Lee, D.T., Preparata, F.P.: Location of a point in a planar subdivision and its applications. SIAM J. Comput. 6(3), 594–606 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  9. Li, J., Wang, W.: Point-in-polygon tests by determining grid center points in advance. In: Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific, pp. 1–7. IEEE (2013)

  10. Li, J., Wang, W., Wu, E.: Point-in-polygon tests by convex decomposition. Comput. Graph. 31(4), 636–648 (2007)

    Article  Google Scholar 

  11. Narkawicz, A, Hagen, G.: Algorithms for collision detection between a point and a moving polygon, with applications to aircraft weather avoidance. In: 16th AIAA Aviation Technology, Integration, and Operations Conference, p. 3598 (2016)

  12. Nordbeck, S., Rystedt, B.: Computer cartography point-in-polygon programs. BIT Numer. Math. 7(1), 39–64 (1967)

    Article  MATH  Google Scholar 

  13. Preparata, F.P., Shamos, M.I.: Introduction. In: Computational Geometry, pp 1–35. Springer (1985)

  14. Rastgoftar, H.: Continuum Deformation of Multi-Agent Systems. Birkhäuser, Cambridge (2016)

    Book  MATH  Google Scholar 

  15. Rastgoftar, H., Jayasuriya, S.: Evolution of multi-agent systems as continua. J. Dyn. Syst. Meas. Control. 136(4), 041014 (2014)

    Article  Google Scholar 

  16. Salomon, K.B.: An efficient point-in-polygon algorithm. Comput. Geosci. 4(2), 173–178 (1978)

    Article  Google Scholar 

  17. Stevens, M.N., Atkins, E.M.: Multi-mode guidance for an independent multicopter geofencing system. In: 16th AIAA Aviation Technology, Integration, and Operations Conference, p. 3150 (2016)

  18. Stevens, M.N., Atkins, E.M.: Layered geofences in complex airspace environments. In: 2018 Aviation Technology, Integration, and Operations Conference, p. 3348 (2018)

  19. Yang, S., Yong, J.H., Sun, J., Gu, H., Paul, J.C.: A point-in-polygon method based on a quasi-closest point. Comput. Geosci. 36(2), 205–213 (2010)

    Article  Google Scholar 

  20. Žalik, B., Kolingerova, I.: A cell-based point-in-polygon algorithm suitable for large sets of points. Comput. Geosci. 27(10), 1135–1145 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mia N. Stevens.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A short version of this paper was presented in ICUAS 2017. This work was supported in part by a subcontract from Soar Technology, Inc. under Phase II SBIR Contract No. FA8650-15-C-2629.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stevens, M.N., Rastgoftar, H. & Atkins, E.M. Geofence Boundary Violation Detection in 3D Using Triangle Weight Characterization with Adjacency. J Intell Robot Syst 95, 239–250 (2019). https://doi.org/10.1007/s10846-018-0930-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-018-0930-5

Keywords

Navigation