Data collection planning with non-zero sensing distance for a budget and curvature constrained unmanned aerial vehicle

Abstract

Data collection missions are one of the many effective use cases of unmanned aerial vehicles (UAVs), where the UAV is required to visit a predefined set of target locations to retrieve data. However, the flight time of a real UAV is time constrained, and therefore only a limited number of target locations can typically be visited within the mission. In this paper, we address the data collection planning problem called the Dubins Orienteering Problem with Neighborhoods (DOPN), which sets out to determine the sequence of visits to the most rewarding subset of target locations, each with an associated reward, within a given travel budget. The objective of the DOPN is thus to maximize the sum of the rewards collected from the visited target locations using a budget constrained path between predefined starting and ending locations. The variant of the orienteering problem addressed here uses curvature-constrained Dubins vehicle model for planning the data collection missions for UAV. Moreover, in the DOPN, it is also assumed that the data, and thus the reward, may be collected from a close neighborhood sensing distance around the target locations, e.g., taking a snapshot by an onboard camera with a wide field of view, or using a sensor with a long range. We propose a novel approach based on the Variable Neighborhood Search (VNS) metaheuristic for the DOPN, in which combinatorial optimization of the sequence for visiting the target locations is simultaneously addressed with continuous optimization for finding Dubins vehicle waypoints inside the neighborhoods of the visited targets. The proposed VNS-based DOPN algorithm is evaluated in numerous benchmark instances, and the results show that it significantly outperforms the existing methods in both solution quality and computational time. The practical deployability of the proposed approach is experimentally verified in a data collection scenario with a real hexarotor UAV.

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Notes

  1. 1.

    Available online https://www.mech.kuleuven.be/en/cib/op/#OP.

  2. 2.

    https://archive.org/download/vns-dopn/results.pdf.

  3. 3.

    See http://mrs.felk.cvut.cz/mbzirc for examples of the experimental deployment of the system.

References

  1. Báča, T., Loianno, G., & Saska, M. (2016). Embedded model predictive control of unmanned micro aerial vehicles. In International conference on methods and models in automation and robotics (MMAR), pp. 992–997.

  2. Best, G., Faigl, J., & Fitch, R. (2016). Multi-robot path planning for budgeted active perception with self-organising maps. In IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 3164–3171.

  3. Best, G., Faigl, J., & Fitch, R. (2018). Online planning for multi-robot active perception with self-organising maps. Autonomous Robots, 42(4), 715–738.

    Article  Google Scholar 

  4. Chao, I. M., Golden, B. L., & Wasil, E. A. (1996a). A fast and effective heuristic for the orienteering problem. European Journal of Operational Research, 88(3), 475–489.

    MATH  Article  Google Scholar 

  5. Chao, I. M., Golden, B. L., & Wasil, E. A. (1996b). The team orienteering problem. European Journal of Operational Research, 88(3), 464–474.

    MATH  Article  Google Scholar 

  6. Cohen, I., Epstein, C., Isaiah, P., Kuzi, S., & Shima, T. (2017). Discretization-based and look-ahead algorithms for the dubins traveling salesperson problem. IEEE Transactions on Automation Science and Engineering, 14(1), 383–390.

    Article  Google Scholar 

  7. Dubins, L. E. (1957). On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. American Journal of Mathematics, 79(3), 497–516.

    MathSciNet  MATH  Article  Google Scholar 

  8. Ergezer, H., & Leblebicioğlu, K. (2014). 3D path planning for multiple UAVs for maximum information collection. Journal of Intelligent & Robotic Systems, 73(1), 737–762.

    Article  Google Scholar 

  9. Faigl, J. (2017). On self-organizing maps for orienteering problems. In 2017 international joint conference on neural networks (IJCNN), pp. 2611–2620.

  10. Faigl, J., & Hollinger, G. A. (2014). Unifying multi-goal path planning for autonomous data collection. In IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 2937–2942.

  11. Faigl, J., & Hollinger, G. A. (2018). Autonomous data collection using a self-organizing map. IEEE Transactions on Neural Networks and Learning Systems, 29(5), 1703–1715.

    MathSciNet  Article  Google Scholar 

  12. Faigl, J., & Pěnička, R. (2017). On close enough orienteering problem with Dubins vehicle. In IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 5646–5652.

  13. Faigl, J., Pěnička, R., & Best, G. (2016). Self-organizing map-based solution for the orienteering problem with neighborhoods. In IEEE international conference on systems, man, and cybernetics (SMC), pp. 1315–1321.

  14. Faigl, J., & Váňa, P. (2017). Unsupervised learning for surveillance planning with team of aerial vehicles. In International joint conference on neural networks (IJCNN), pp. 4340–4347.

  15. Faigl, J., & Váňa, P. (2018). Surveillance planning with Bézier curves. IEEE Robotics and Automation Letters, 3(2), 750–757.

    Article  Google Scholar 

  16. Faigl, J., Váňa, P., Saska, M., Báča, T., & Spurný, V. (2017). On solution of the Dubins touring problem. In European conference on mobile robotics (ECMR), pp. 151–156.

  17. Fischetti, M., González, J. J. S., & Toth, P. (1998). Solving the orienteering problem through branch-and-cut. INFORMS Journal on Computing, 10(2), 133–148.

    MathSciNet  MATH  Article  Google Scholar 

  18. Gunawan, A., Lau, H. C., & Vansteenwegen, P. (2016). Orienteering problem: A survey of recent variants, solution approaches and applications. European Journal of Operational Research, 255(2), 315–332.

    MathSciNet  MATH  Article  Google Scholar 

  19. Hansen, P., & Mladenović, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130(3), 449–467.

    MathSciNet  MATH  Article  Google Scholar 

  20. Helsgaun, K. (2000). An effective implementation of the Lin–Kernighan traveling salesman heuristic. European Journal of Operational Research, 126(1), 106–130.

    MathSciNet  MATH  Article  Google Scholar 

  21. Isaacs, J. T., Klein, D. J., & Hespanha, J. P. (2011). Algorithms for the traveling salesman problem with neighborhoods involving a Dubins vehicle. In American Control Conference (ACC), pp. 1704–1709.

  22. Jawhar, I., Mohamed, N., Al-Jaroodi, J., & Zhang, S. (2014). A framework for using unmanned aerial vehicles for data collection in linear wireless sensor networks. Journal of Intelligent & Robotic Systems, 74(1), 437–453.

    Article  Google Scholar 

  23. Jorgensen, S., Chen, R. H., Milam, M. B., & Pavone, M. (2018). The team surviving orienteers problem: Routing teams of robots in uncertain environments with survival constraints. Autonomous Robots, 42(4), 927–952.

    Article  Google Scholar 

  24. Le Ny, J., Frazzoli, E., & Feron, E. (2007). The curvature-constrained traveling salesman problem for high point densities. In IEEE conference on decision and control, pp. 5985–5990.

  25. Lugo-Cárdenas, I., Flores, G., Salazar, S., & Lozano, R. (2014). Dubins path generation for a fixed wing UAV. In International conference on unmanned aircraft systems (ICUAS), pp. 339–346.

  26. Meier, L., Tanskanen, P., Heng, L., Lee, G. H., Fraundorfer, F., & Pollefeys, M. (2012). PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision. Autonomous Robots, 33(1), 21–39.

    Article  Google Scholar 

  27. Mladenović, N., Dražić, M., Kovačevic-Vujčić, V., & Čangalović, M. (2008). General variable neighborhood search for the continuous optimization. European Journal of Operational Research, 191(3), 753–770.

    MathSciNet  MATH  Article  Google Scholar 

  28. Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & Operations Research, 24(11), 1097–1100.

    MathSciNet  MATH  Article  Google Scholar 

  29. Nguyen, J. L., Lawrance, N. R. J., Fitch, R., & Sukkarieh, S. (2016). Real-time path planning for long-term information gathering with an aerial glider. Autonomous Robots, 40(6), 1017–1039.

    Article  Google Scholar 

  30. Noon, C. E., & Bean, J. C. (1993). An efficient transformation of the generalized traveling salesman problem. INFOR: Information Systems and Operational Research, 31(1), 39–44.

    MATH  Google Scholar 

  31. Oberlin, P., Rathinam, S., & Darbha, S. (2010). Today’s traveling salesman problem. IEEE Robotics Automation Magazine, 17(4), 70–77.

    Article  Google Scholar 

  32. Obermeyer, K. J. (2009). Path planning for a UAV performing reconnaissance of static ground targets in terrain. In AIAA Guidance, Navigation, and Control Conference.

  33. Obermeyer, K. J., Oberlin, P., & Darbha, S. (2010). Sampling-based roadmap methods for a visual reconnaissance UAV. In AIAA Guidance, Navigation, and Control Conference.

  34. Pěnička, R., Faigl, J., Váňa, P., & Saska, M. (2017a). Dubins orienteering problem. IEEE Robotics and Automation Letters, 2(2), 1210–1217.

    Article  Google Scholar 

  35. Pěnička, R., Faigl, J., Váňa, P., & Saska, M. (2017b). Dubins orienteering problem with neighborhoods. In International conference on unmanned aircraft systems (ICUAS), pp. 1555–1562.

  36. Ramesh, R., & Brown, K. M. (1991). An efficient four-phase heuristic for the generalized orienteering problem. Computers & Operations Research, 18(2), 151–165.

    MathSciNet  Article  Google Scholar 

  37. Ramesh, R., Yoon, Y. S., & Karwan, M. H. (1992). An optimal algorithm for the orienteering tour problem. ORSA Journal on Computing, 4(2), 155–165.

    MATH  Article  Google Scholar 

  38. Savla, K., Frazzoli, E., & Bullo, F. (2005). On the point-to-point and traveling salesperson problems for Dubins’ vehicle. In American Control Conference (ACC), Vol. 2, pp. 786–791.

  39. Schilde, M., Doerner, K. F., Hartl, R. F., & Kiechle, G. (2009). Metaheuristics for the bi-objective orienteering problem. Swarm Intelligence, 3(3), 179–201.

    Article  Google Scholar 

  40. Sevkli, Z., & Sevilgen, F. E. (2006). Variable neighborhood search for the orienteering problem. In International symposium on computer and information sciences (ISCIS), pp. 134–143.

  41. Tersus-GNSS. (2018). PRECIS-BX305 GNSS RTK Board. https://www.tersus-gnss.com. Accessed 21 July 2018.

  42. Thakur, D., Likhachev, M., Keller, J., Kumar, V., Dobrokhodov, V., Jones, K., Wurz, J., & Kaminer, I. (2013). Planning for opportunistic surveillance with multiple robots. In IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 5750–5757.

  43. Tokekar, P., Karnad, N., & Isler, V. (2014). Energy-optimal trajectory planning for car-like robots. Autonomous Robots, 37(3), 279–300.

    Article  Google Scholar 

  44. Tsiligirides, T. (1984). Heuristic methods applied to orienteering. The Journal of the Operational Research Society, 35(9), 797–809.

    Article  Google Scholar 

  45. Tsiogkas, N., & Lane, D. M. (2018). DCOP: Dubins correlated orienteering problem optimizing sensing missions of a nonholonomic vehicle under budget constraints. IEEE Robotics and Automation Letters, 3(4), 2926–2933.

    Article  Google Scholar 

  46. Váňa, P., & Faigl, J. (2015). On the Dubins traveling salesman problem with neighborhoods. In IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 4029–4034.

  47. Váňa, P., & Faigl, J. (2018). Optimal solution of the generalized Dubins interval problem. In Robotics: Science and Systems (RSS).

  48. Vansteenwegen, P. (2018). The orienteering problem: Test instances. Department of Mechanical Engineering, University of Leuven. http://www.mech.kuleuven.be/en/cib/op/#OP. Accessed 21 July 2018.

  49. Vansteenwegen, P., Souffriau, W., & Oudheusden, D. V. (2011). The orienteering problem: A survey. European Journal of Operational Research, 209(1), 1–10.

    MathSciNet  MATH  Article  Google Scholar 

  50. Wang, C., Ma, F., Yan, J., De, D., & Das, S. K. (2015). Efficient aerial data collection with UAV in large-scale wireless sensor networks. International Journal of Distributed Sensor Networks, 11(11), 286080.

    Article  Google Scholar 

  51. Wren, A., & Holliday, A. (1972). Computer scheduling of vehicles from one or more depots to a number of delivery points. Operational Research Quarterly (1970–1977), 23(3), 333–344.

    Article  Google Scholar 

  52. Yu, J., Schwager, M., & Rus, D. (2016). Correlated orienteering problem and its application to persistent monitoring tasks. IEEE Transactions on Robotics, 32(5), 1106–1118. https://doi.org/10.1109/TRO.2016.2593450.

    Article  Google Scholar 

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Correspondence to Robert Pěnička.

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This work was supported by the Czech Science Foundation under research Project Nos. 16-24206S, 17-16900Y, and 19-20238S. The authors acknowledge the support of the OP VVV funded Project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”. Access to computing at National Grid Infrastructure MetaCentrum, provided under the CESNET LM2015042 programme, is greatly appreciated. The support of CTU in Prague Grant No. SGS17/187/13 is also gratefully acknowledged.

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Pěnička, R., Faigl, J., Saska, M. et al. Data collection planning with non-zero sensing distance for a budget and curvature constrained unmanned aerial vehicle. Auton Robot 43, 1937–1956 (2019). https://doi.org/10.1007/s10514-019-09844-5

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Keywords

  • Unmanned aerial vehicles
  • Non-holonomic motion planning
  • Data collection planning
  • Orienteering problem