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Multi-Robot Mission Planning with Static Energy Replenishment

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Abstract

The success of numerous long-term robotic explorations in the air, on the ground, and under the water is dependent on the ability of robots to operate for an extended time. The long-term ubiquitous operation of robots hinges on smart energy consumption and the replenishment of the robots. This paper provides a heuristic method for planning missions that extend over multiple battery lives of working robots. This method simultaneously generates energy efficient trajectories for multiple robots, and schedules energy cycling using static charging stations through the mission. The mission planning algorithm accounts for environmental obstacles, current, and can adapt to a priority search distribution. The simulation results for a scenario similar to the MH370 airplane search mission demonstrate the effectiveness of the developed algorithm in area coverage and handling environmental constraints. The robustness of the developed method is evaluated through a Monte Carlo simulation. In addition, the proposed algorithm is tested in simulation environment in Gazebo and implemented and experimentally validated for an in-lab aerial coverage scenario with an obstacle and a priority mission area.

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References

  1. Angrisani, L., d’Alessandro, G., D’Arco, M., Accardo, D., Fasano, G.: A contactless induction system for battery recharging of autonomous vehicles. In: Proc. IEEE Conf. MetroAeroSpace, pp. 494–499 (2014)

  2. Angrisani, L., d’Alessandro, G., D’Arco, M., Paciello, V., Pietrosanto, A.: Autonomous recharge of drones through an induction based power transfer system. In: Proc. IEEE Int. Workshop on Measurements & Networking, pp. 1–6 (2015)

  3. Batalin, M.A., Sukhatme, G.S.: The design and analysis of an efficient local algorithm for coverage and exploration based on sensor network deployment. IEEE Trans. Robot. 23(4), 661–675 (2007)

    Article  Google Scholar 

  4. Brighenti, A., Zugno, L., Mattiuzzo, F., Sperandio, A.: EURODOCKER-a universal docking-downloading recharging system for AUVs: Conceptual design results. In: Proc. IEEE Conf. OCEANS, pp. 1463–1467 (1998)

  5. Cena, J.M.: Power transfer efficiency of mutually coupled coils in an aluminum AUV hull. Master’s thesis, Dept. Elect. Eng., Naval Postgraduate School, Monterey, CA USA (2013)

  6. Choset, H.: Coverage for robotics–a survey of recent results. Ann. Math. Artif. Intell. 31(1-4), 113–126 (2001)

    Article  MATH  Google Scholar 

  7. Gabriely, Y., Rimon, E.: Spanning-tree based coverage of continuous areas by a mobile robot. Ann. Math. Artif. Intell. 31(1-4), 77–98 (2001)

    Article  MATH  Google Scholar 

  8. Galceran, E., Carreras, M.: A survey on coverage path planning for robotics. Robot. Auton. Syst. 61(12), 1258–1276 (2013)

    Article  Google Scholar 

  9. Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm. J. R. Statist. Soc. Series C (Appl. Statist.) 28(1), 100–108 (1979)

    MATH  Google Scholar 

  10. Ho, W., Ho, G.T., Ji, P., Lau, H.C.: A hybrid genetic algorithm for the multi-depot vehicle routing problem. Eng. Appl. Artif. Intel. 21(4), 548–557 (2008)

    Article  Google Scholar 

  11. Hu, Y., Yang, S.X.: A knowledge based genetic algorithm for path planning of a mobile robot. In: Proc. IEEE Int. Conf. Robot. Autom., vol. 5, pp. 4350–4355 (2004)

  12. Kapanoglu, M., Alikalfa, M., Ozkan, M., Yazıcı, A., Parlaktuna, O.: A pattern-based genetic algorithm for multi-robot coverage path planning minimizing completion time. J. Intell. Manuf. 23(4), 1035–1045 (2012)

    Article  Google Scholar 

  13. Keller, J., Thakur, D., Likhachev, M., Gallier, J., Kumar, V.: Coordinated path planning for fixed-wing UAS conducting persistent surveillance missions. IEEE Trans. Autom. Sci. Eng. 14(1), 17–24 (2017)

    Article  Google Scholar 

  14. Kirk, J.: Multiple traveling salesmen problem - genetic algorithm, MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/19049-multiple-traveling-salesmen-problem-genetic-algorithm (2014)

  15. Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: A tutorial. Reliab. Eng. Syst. Safe. 91(9), 992–1007 (2006)

    Article  Google Scholar 

  16. Kularatne, D., Bhattacharya, S., Hsieh, M.A.: Time and energy optimal path planning in general flows. In: Robotics: Science and Systems (2016)

  17. Leahy, K., Zhou, D., Vasile, C.I., Oikonomopoulos, K., Schwager, M., Belta, C.: Persistent surveillance for unmanned aerial vehicles subject to charging and temporal logic constraints. Auton. Robot. 40(8), 1363–1378 (2016)

    Article  Google Scholar 

  18. Li, B., Moridian, B., Mahmoudian, N.: Underwater multi-robot persistent area coverage mission planning. In: Proc. MTS/IEEE Conf. OCEANS, pp. 1–6 (2016)

  19. Litus, Y., Vaughan, R.T., Zebrowski, P.: The frugal feeding problem: Energy-efficient, multi-robot, multi-place rendezvous. In: Proc. IEEE Int. Conf. Robot. Autom., pp. 27–32. IEEE (2007)

  20. Litus, Y., Zebrowski, P., Vaughan, R.T.: A distributed heuristic for energy-efficient multirobot multiplace rendezvous. IEEE Trans. Robot. 25(1), 130–135 (2009)

    Article  Google Scholar 

  21. Luo, C., Yang, S.X., Stacey, D.A., Jofriet, J.C.: A solution to vicinity problem of obstacles in complete coverage path planning. In: Proc. IEEE Int. Conf. Robot. Autom., vol. 1, pp. 612–617. IEEE (2002)

  22. Maini, P., Sujit, P.: On cooperation between a fuel constrained UAV and a refueling UGV for large scale mapping applications. In: Proc. International Conference on Unmanned Aircraft Systems, pp. 1370–1377. IEEE (2015)

  23. Mathew, N., Smith, S.L., Waslander, S.L.: A graph-based approach to multi-robot rendezvous for recharging in persistent tasks. In: Proc. IEEE Int. Conf. Robot. Autom., pp. 3497–3502 (2013)

  24. Mathew, N., Smith, S.L., Waslander, S.L.: Multirobot rendezvous planning for recharging in persistent tasks. IEEE Trans. Robot. 31(1), 128–142 (2015)

    Article  Google Scholar 

  25. Mitchell, D., Corah, M., Chakraborty, N., Sycara, K., Michael, N.: Multi-robot long-term persistent coverage with fuel constrained robots. In: Proc. IEEE Int. Conf. Robot. Autom., pp. 1093–1099 (2015)

  26. Mulgaonkar, Y., Kumar, V.: Autonomous charging to enable long-endurance missions for small aerial robots. In: Proceedings of SPIE-DSS p. 90831S (2014)

  27. Palacios-Gasós, J.M., Montijano, E., Sagüés, C., Llorente, S.: Distributed coverage estimation and control for multirobot persistent tasks. IEEE Trans. Robot. 32(6), 1444–1460 (2016)

    Article  Google Scholar 

  28. Pasqualetti, F., Durham, J.W., Bullo, F.: Cooperative patrolling via weighted tours: Performance analysis and distributed algorithms. IEEE Trans. Robot. 28(5), 1181–1188 (2012)

    Article  Google Scholar 

  29. Rao, D., Williams, S.B.: Large-scale path planning for underwater gliders in ocean currents. In: Proc. Australasian Conference on Robotics and Automation (2009)

  30. Smith, S.L., Schwager, M., Rus, D.: Persistent robotic tasks: Monitoring and sweeping in changing environments. IEEE Trans. Robot. 28(2), 410–426 (2012)

    Article  Google Scholar 

  31. Soltero, D.E., Schwager, M., Rus, D.: Decentralized path planning for coverage tasks using gradient descent adaptive control. Int. J. Robot. Res. 33(3), 401–425 (2014)

    Article  Google Scholar 

  32. Song, C., Liu, L., Feng, G., Wang, Y., Gao, Q.: Persistent awareness coverage control for mobile sensor networks. Automatica 49(6), 1867–1873 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  33. Stokey, R., Purcell, M., Forrester, N., Austin, T., Goldsborough, R., Allen, B., Von Alt, C.: A docking system for REMUS, an autonomous underwater vehicle. In: Proc. MTS/IEEE Conf. OCEANS, pp. 1132–1136 (1997)

  34. Surekha, P., Sumathi, S.: Solution to multi-depot vehicle routing problem using genetic algorithms. World Appl. Program. 1(3), 118–131 (2011)

    Google Scholar 

  35. Yehoshua, R., Agmon, N., Kaminka, G.A.: Robotic adversarial coverage of known environments. Int. J. Robot. Res. 35(12), 1419–1444 (2016)

    Article  Google Scholar 

  36. Zebrowski, P., Vaughan, R.T.: Recharging robot teams: A tanker approach. In: Proc. IEEE Int. Conf. Adv. Robot., pp. 803–810 (2005)

  37. Zelinsky, A., Jarvis, R.A., Byrne, J., Yuta, S.: Planning paths of complete coverage of an unstructured environment by a mobile robot. Proc. IEEE Int. Conf. Adv. Robot. 13, 533–538 (1993)

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Correspondence to Nina Mahmoudian.

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This material is based upon work supported by NSF 1453886 and ONR N00014-15-1-2599.

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Li, B., Moridian, B., Kamal, A. et al. Multi-Robot Mission Planning with Static Energy Replenishment. J Intell Robot Syst 95, 745–759 (2019). https://doi.org/10.1007/s10846-018-0897-2

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  • DOI: https://doi.org/10.1007/s10846-018-0897-2

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