Advertisement

Autonomous Robots

, Volume 42, Issue 4, pp 825–851 | Cite as

Complete 3-D dynamic coverage in energy-constrained multi-UAV sensor networks

  • William BentzEmail author
  • Tru Hoang
  • Enkhmurun Bayasgalan
  • Dimitra Panagou
Article
Part of the following topical collections:
  1. Special Issue: Online Decision Making in Multi-Robot Coordination

Abstract

This paper considers dynamic coverage control of multiple power-constrained agents subject to 3D rigid body kinematics. The agents are deployed to patrol a domain until the entire space has reached a satisfactory level of coverage. This is achieved through the gathering of information by a forward-facing sensor footprint, modelled as an anisotropic spherical sector. Coverage and collision avoidance guarantees are met by a hybrid controller consisting of four operating modes: local coverage, global coverage, waypoint scan and subdomain transfer. Energy-aware methods are encoded into the global coverage state to shift the bulk of spatial redistribution onto less constrained agents. Additionally, a novel domain partitioning strategy is used that directs individual agents to explore within concentric hemispherical shells around a centralized charging station. This results in flight paths that are guaranteed to terminate at the charging station in the limit that agent batteries expire. The efficacy of this algorithm is presented through experimental trials with three agents in an indoor environment. Simulations are provided for ten agents.

Keywords

Coverage Multi-robot cooperation Aerial robots Autonomous vehicle 

Supplementary material

Supplementary material 1 (mp4 258209 KB)

References

  1. Abazeed, M., Faisal, N., Zubair, S., & Ali, A. (2013). Routing protocols for wireless multimedia sensor network: A survey. Journal of Sensors, 2013.Google Scholar
  2. Arajo, J. F., Sujit, P. B., & Sousa, J. B. (2013). Multiple UAV area decomposition and coverage. In 2013 IEEE symposium on computational intelligence for security and defense applications (CISDA) (pp. 30–37).Google Scholar
  3. Atinc, G., Stipanovíc, D. M., & Voulgaris, P. G. (2014). Supervised coverage control of multi-agent systems. Automatica, 50(11), 2936–2942.MathSciNetCrossRefzbMATHGoogle Scholar
  4. Beard, R. W. (2008). Quadrotor dynamics and control. Lecture notes. https://www.researchgate.net/publication/265825340.
  5. Bentz, W., & Panagou, D. (2016). An energy-aware redistribution method for multi-agent dynamic coverage networks. In Proceedings of of the 2016 IEEE conference on decision and control, Las Vegas, NV.Google Scholar
  6. Bentz, W., & Panagou, D. (2017). 3D dynamic coverage and avoidance control in power-constrained UAV surveillance networks. In Proceedings of of the 2017 international conference on unmanned aircraft systems, Miami, FL.Google Scholar
  7. Berman, E., Fladeland, M., Liem, J., Kolyer, R., & Gupta, M. (2012). Greenhouse gas analyzer for measurements of carbon dioxide, methane, and water vapor aboard an unmanned aerial vehicle. Sensors and Actuators B: Chemical, 169, 128–135.CrossRefGoogle Scholar
  8. Chahine, M. T., Chen, L., Dimotakis, P., Jiang, X., Li, Q., Olsen, E. T., et al. (2008). Satellite remote sounding of mid-tropospheric CO\(_2\). Geophysical Research Letters. doi: 10.1029/2008GL035022.
  9. Cheng, P., Keller, J., & Kumar, V. (2008). Time-optimal UAV trajectory planning for 3D urban structure coverage. In Proceedings of of the 2008 IEEE/RSJ international conference on intelligent robots and systems, Nice, France (pp. 2750–2757).Google Scholar
  10. Cole, D. T., Goktogan, A. H., Thompson, P., & Sukkarieh, S. (2009). Mapping and tracking. IEEE Robotics Automation Magazine, 16(2), 22–34.CrossRefGoogle Scholar
  11. Cortes, J., Martínez, S., Karatas, T., & Bullo, F. (2004). Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation, 20(2), 243–255.CrossRefGoogle Scholar
  12. Franco, C., Stipanovi, D. M., Lpez-Nicols, G., Sags, C., & Llorente, S. (2015). Persistent coverage control for a team of agents with collision avoidance. European Journal of Control, 22, 30–45.MathSciNetCrossRefGoogle Scholar
  13. Hardy, G. H., Littlewood, J. E., & Pólya, G. (1952). Inequalities (p. 43). Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  14. Hokayem, P., Stipanovíc, D., & Spong, M. (2007). On persistent coverage control. In Proceedings of of the 2007 IEEE conference on decision and control, New Orleans, LA, USA (pp. 6130–6135).Google Scholar
  15. Hübel, N., Hirche, S., Gusrialdi, A., Hatanaka, T., Fujita, M., & Sawodny, O. (2008). Coverage control with information decay in dynamic environments. In Proceedings of the 17th IFAC World Congress, Seoul, South Korea (pp. 4180–4185).Google Scholar
  16. Hussein, I. I., & Stipanović, D. M. (2007). Effective coverage control for mobile sensor networks with guaranteed collision avoidance. IEEE Transactions on Control Systems Technology, 15(4), 642–657.CrossRefGoogle Scholar
  17. Kaczor, W., & Nowak, M. (2003). Problems in mathematical analysis II. Providence: American Mathematical Society.zbMATHGoogle Scholar
  18. Kwok, A., & Martínez, S. (2008). Deployment algorithms for a power-constrained mobile sensor network. In Proceedings of the 2008 IEEE international conference on robotics and automation, Pasadena, CA (pp. 140–145).Google Scholar
  19. Leahy, K., Zhou, D., Vasile, C. I., Oikonomopoulos, K., Schwager, M., & Belta, C. (2016). Persistent surveillance for unmanned aerial vehicles subject to charging and temporal logic constraints. Autonomous Robots, 40(8), 1363–1378.CrossRefGoogle Scholar
  20. Liang, J., Liu, M., & Kui, X. (2014). A survey of coverage problems in wireless sensor networks. Sensors and Transducers, 163, 240–246.Google Scholar
  21. Liu, B., Dousse, O., Nain, P., & Towsley, D. (2013). Dynamic coverage of mobile sensor networks. IEEE Transactions on Parallel and Distributed systems, 24(2), 301–311.CrossRefGoogle Scholar
  22. Lygeros, J., Shankar S., & Claire T. (2012). Hybrid systems: Foundations, advanced topics and applications. Springer Verlag. http://inst.eecs.berkeley.edu/~ee291e/sp12/handouts/book.pdf.
  23. Ma, X., Jiao, Z., Wang, Z., & Panagou, D. (2016). 3D decentralized prioritized motion planning and coordination for high-density operations of micro aerial vehicles. IEEE Transactions on Control Systems Technology (to appear). http://www-personal.umich.edu/~dpanagou/assets/documents/XMa_TCST17.pdf.
  24. Mitchell, D., Corah, M., Chakraborty, N., Sycara, K., & Michael, N. (2015). Multi-robot long-term persistent coverage with fuel constrained robots. In Proceedings of the 2015 IEEE international conference on robotics and automation (ICRA) (pp. 1093–1099).Google Scholar
  25. Nam, L. H., Huang, L., Li, X. J., & Xu, J. F. (2016). An approach for coverage path planning for UAVs. In 2016 IEEE 14th international workshop on advanced motion control (AMC) (pp. 411–416).Google Scholar
  26. Oktug, S., Khalilov, A., & Tezcan, H. (2008). 3D coverage analysis under heterogeneous deployment strategies in wireless sensor networks. In Proceedings of the 2008 fourth advanced international conference on telecommunications (pp. 199–204).Google Scholar
  27. Panagou, D., Stipanović, D. M., & Voulgaris, P. G. (2016a). Distributed coordination control for multi-robot networks using Lyapunov-like barrier functions. IEEE Transactions on Automatic Control, 61(3), 617–632.MathSciNetCrossRefzbMATHGoogle Scholar
  28. Panagou, D., Stipanović, D. M., & Voulgaris, P. G. (2016b). Distributed dynamic coverage and avoidance control under anisotropic sensing. IEEE Transactions on Control of Network Systems. doi: 10.1109/TCNS.2016.2576403.
  29. Piciarelli, C., Micheloni, C., & Foresti, G. L. (2011). Automatic reconfiguration of video sensor networks for optimal 3D coverage. In Proceedings of the 2011 Fifth ACM/IEEE international conference on distributed smart cameras (ICDSC), IEEE (pp. 1–6).Google Scholar
  30. Slattery, J. C. (1999). Advanced transport phenomena. Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  31. Smith, S. L., Schwager, M., & Rus, D. (2012). Persistent robotic tasks: Monitoring and sweeping in changing environments. IEEE Transactions on Robotics, 28(2), 410–426.CrossRefGoogle Scholar
  32. Song, C., Liu, L., Feng, G., Wang, Y., & Gao, Q. (2013). Persistent awareness coverage control for mobile sensor networks. Automatica, 49(6), 1867–1873.MathSciNetCrossRefzbMATHGoogle Scholar
  33. Stipanović, D. M., Valicka, C., Tomlin, C. J., & Bewley, T. R. (2013). Safe and reliable coverage control. Numerical Algebra, Control and Optimization, 3, 31–48.MathSciNetCrossRefzbMATHGoogle Scholar
  34. Tisdale, J., Kim, Z., & Hedrick, J. K. (2009). Autonomous UAV path planning and estimation. IEEE Robotics Automation Magazine, 16(2), 35–42.CrossRefGoogle Scholar
  35. Wills, A. G., & Heath, W. P. (2002). A recentred barrier for constrained receding horizon control. In Proceedings of the 2002 American control conference (IEEE Cat. No. CH37301) (Vol. 5, pp. 4177–4182).Google Scholar
  36. Xie, L., & Zhang, X. (2013). 3D clustering-based camera wireless sensor networks for maximizing lifespan with minimum coverage rate constraint. In Proceedings of the 2013 IEEE global communications conference (GLOBECOM), IEEE (pp. 298–303).Google Scholar
  37. Yang, M., Kim, D., Li, D., Chen, W., Du, H., & Tokuta, A. O. (2013). Sweep-coverage with energy-restricted mobile wireless sensor nodes. Berlin: Springer.CrossRefGoogle Scholar
  38. Zhang, Y., Li, X., Yang, J., Liu, Y., Xiong, N., & Vasilakos, A. V. (2013). A real-time dynamic key management for hierarchical wireless multimedia sensor network. Multimedia Tools and Applications, 67(1), 97–117.CrossRefGoogle Scholar
  39. Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 619–632.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Aerospace EngineeringUniversity of MichiganAnn ArborUSA

Personalised recommendations