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Online schedule for autonomy of multiple unmanned aerial vehicles

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Abstract

An online rectangle based scheduling algorithm (RSA) is developed to improve autonomy of multiple unmanned aerial vehicles (UAVs) to search a field of forest together. The purposes of RSA are to online decide the number of the UAVs to be assigned and to schedule the path for the assigned UAVs to search the missed areas resulted from the previous search. The main ideas of RSA are to cover each separated zone of the missed areas with a rectangle and then to schedule the path to search the rectangles. Thus, RSA is robust against the unknown shapes and sizes of the missed areas. The forest search is applied to verify the online RSA in simulation. The simulation results demonstrate that the online RSA is successful to decide the number of the UAVs to be assigned and to schedule the path for the assigned UAVs to search the missed areas.

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References

  1. Altmann A, Niendorf N, Bednar M, et al. Improved 3D interpolation-based path planning for a fixed-wing unmanned aircraft. J Intell Robot Syst, 2014, 76: 185–197

    Article  Google Scholar 

  2. Bellingham J, Tillerson M, Alighanbari M, et al. Cooperative path planning for multiple UAVs in dynamic and uncertain environments. In: Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, 2002. 2816–2822

    Google Scholar 

  3. Chen Y, Yu J, Su X, et al. Path planning for multi-UAV formation. J Intell Robot Syst, 2015, 77: 229–241

    Article  Google Scholar 

  4. Kaminer I, Yakimenko C, Pascoal A, et al. Path generation, path following and coordinated control for timecritical missions of multiple UAVs. In: Proceedings of American Control Conference, Minneapolis, 2006. 4906–4913

    Google Scholar 

  5. Richards A, Bellingham J, Tillerson M, et al. Coordination and control of multuple UAVs. In: Proceedings of AIAA Guidabce, Navigation abd Control Conference amd Exhibition, Monlerey, 2002. 2002–4588

    Google Scholar 

  6. Cai H, Huang J. Leader-following adaptive consensus of multiple uncertain rigid spacecraft systems. Sci China Inf Sci, 2016, 59: 010201

    Google Scholar 

  7. Ren W, Beard R. On consensus algorithm for double-integrator dynamics. IEEE Trans Autom Control, 2008, 53: 1503–1509

    Article  MathSciNet  Google Scholar 

  8. Chen J, Gan M G, Huang J, et al. Formation control of multiple euler-lagrange systems via null-space-based behavioral control. Sci China Inf Sci, 2016, 59: 010202

    Google Scholar 

  9. Inalhan G, Stipanovic D, Tomlin C. Decentralized optimization with application to multiple aircraft coordination. In: Proceedings of the 41st IEEE International Conference on Decision and Control, Las Vegas, 2002. 1147–1155

    Google Scholar 

  10. Lafferriere G, Williams A, Caughman J, et al. Decentralized control of vehicle formations. Syst Control Lett, 2005, 54: 899–910

    Article  MathSciNet  MATH  Google Scholar 

  11. Liu H Y, Sun F C, Wang S Q. Virtual strategy QoS routing in satellite networks. Sci China Inf Sci, 2016, 59: 092201

    Article  Google Scholar 

  12. Tomlin C J, Pappas G J, Sastry S S. Conflict resolution for air traffic management: a case study in multi-agent hybrid systems. IEEE Trans Autom Control, 1998, 43: 509–521

    Article  MATH  Google Scholar 

  13. Beard R, Mc Lain T. Multiple UAV cooperative search under collision avoidance and limited range communication constraints. In: Proceedings of the 42nd IEEE International Conference on Decision and Control, Maui, 2003. 25–30

    Google Scholar 

  14. Ren W, Beard R. Consensus seeking in multiagent systems with dynamically changing interaction topologies. IEEE Trans Autom Control, 2005, 50: 655–661

    Article  MathSciNet  Google Scholar 

  15. Chrpa L, Osborne H. Towards a trajectory planning concept: augmenting path planning methods by considering speed limit constraints. J Intell Robot Syst, 2014, 76: 243–270

    Article  Google Scholar 

  16. Jadbabaic A, Lin J, Morse A S. Coordination of groups of mobile autonomous agents with neighbor rules. IEEE Trans Autom Control, 2003, 48: 998–1001

    MathSciNet  Google Scholar 

  17. Kogan K, López-Ortiz A, Nikolenko S I, et al. Online scheduling FIFO policies with admission and push-out. Theory Comput Syst, 2016, 58: 322–344

    Article  MathSciNet  MATH  Google Scholar 

  18. Wongpiromsarn T, Topcu U, Murray R M. Synthesis of control protocols for autonomous systems. Unmanned Syst, 2013, 1: 21–39

    Article  Google Scholar 

  19. Kopeikin A N, Ponda S S, Johnson L B, et al. Dynamic mission planning for communication control in multiple unmanned aircraft teams. Unmanned Syst, 2013, 1: 41–58

    Article  Google Scholar 

  20. Leoff J, Ackermann H, Küfer K H. Time-hierarchical scheduling. J Sched, 2016, 19: 215–225

    Article  MathSciNet  MATH  Google Scholar 

  21. Peng K, Pang T, Lin F, et al. Autonomous mission execution for multiple unmanned aerial vehicles with hierarchicaldistributed methodology. In: Proceedings of the 11th IEEE International Conference on Control and Automation, Taichung, 2014. 1369–1374

    Chapter  Google Scholar 

  22. Zhang B, Mao Z, Liu W, et al. Geometric reinforcement learning for path planning of UAVs. J Intell Robot Syst, 2015, 77: 391–402

    Article  Google Scholar 

  23. Cailhol S, Fillatreau P, Fourquet J Y, et al. A hierarchic approach for path planning in virtual reality. Int J Interact Des Manuf, 2015, 9: 291–302

    Article  Google Scholar 

  24. Chen H, Lee J. Path planning of 5-DOF manipulator based on maximum mobility. Int J Precis Eng Manuf, 2014, 15: 45–52

    Article  Google Scholar 

  25. Lim S H, Han C S. Operational space path planning of the dual-arm robot for the assembly task. Int J Precis Eng Manuf, 2014, 15: 2071–2076

    Article  Google Scholar 

  26. Doherty P, Heintz F, Kvarnström J. High-level mission specification and planning for collaborative unmanned aircraft systems using delegation. Unmanned Syst, 2013, 1: 75–119

    Article  Google Scholar 

  27. Epstein L, Jez L, Sgall J, et al. Online scheduling of jobs with fixed start times on related machines. Algorithmica, 2016, 74: 156–176

    Article  MathSciNet  MATH  Google Scholar 

  28. Li W, Yuan J. LPT online strategy for parallel-machine scheduling with kind release times. Optim Lett, 2016, 10: 159–168

    Article  MathSciNet  MATH  Google Scholar 

  29. Liu H, Yuan J, Li H. Online scheduling of equal length jobs on unbounded parallel batch processing machines with limited restart. J Comb Optim, 2016, 31: 1609–1622

    Article  MathSciNet  MATH  Google Scholar 

  30. Xu J, Liu Z H. An optimal online algorithm for scheduling on two parallel machines with GoS eligibility constraints. Oper Res Soc China, 2016, 4: 371–377

    Article  MathSciNet  Google Scholar 

  31. Putzer H, Onken R. COSA — a generic cognitive system architecture based on a cognitive model of human behavior. Cogn Tech Work, 2003, 5: 140–151

    Article  Google Scholar 

  32. Uhrmann J, Schulte A. Task-based guidance of multiple UAV using cognitive automation. In: Proceedings of International Conference on Advanced Cognitive Technologies and Applications, Rome, 2011. 47–52

    Google Scholar 

  33. Tchernykh A, Lozano L, Schwiegelshohn U, et al. Online bi-objective scheduling for iaas clouds ensuring quality of service. J Grid Comput, 2016, 14: 5–22

    Article  Google Scholar 

  34. Deb S, Fong S, Tian Z, et al. Finding approximate solutions of NP-hard optimization and TSP problems using elephant search algorithm. J Supercomput, 2016, 72: 3960–3992

    Article  Google Scholar 

  35. Laporte G. The traveling salesman problem: an overview of exact and approximate algorithms. Eur J Oper Res, 1992, 59: 231–247

    Article  MATH  Google Scholar 

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Correspondence to Kemao Peng.

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Peng, K., Lin, F. & Chen, B.M. Online schedule for autonomy of multiple unmanned aerial vehicles. Sci. China Inf. Sci. 60, 072203 (2017). https://doi.org/10.1007/s11432-016-9025-9

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  • DOI: https://doi.org/10.1007/s11432-016-9025-9

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