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Multi-MAV Autonomous Full Coverage Search in Cluttered Forest Environments

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Abstract

This paper is concerned with autonomous forest full coverage search using multiple micro aerial vehicles (MAVs). Due to the complex and cluttered environment, i.e., many obstacles under the forest canopy, it is quite challenging to achieve full coverage search using fully autonomous MAVs, e.g., quadrotors. In this work, we propose a two-stage multi-MAV forest search strategy. The first batch of MAVs provides a coarse search and mapping result using pre-defined or auto-generated paths. Based on that, the second batch of MAVs continues to search the multiple isolated regions missed by the first batch. The main difficulties fall in the autonomous task allocation and optimal cooperative coverage path planning for the second batch of MAVs, to achieve the full coverage goal. To address this problem, a task allocation algorithm based on the branch and bound principle is introduced to find the optimal search order of the missed regions. Furthermore, an optimal coverage path planning algorithm considering obstacle avoidance is proposed to cover each region. Simulation results show that our proposed method improves the efficiency of coverage path planning for cooperative search and guarantees full area coverage.

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References

  1. Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., Grixa, I., Ruess, F., Suppa, M., Burschka, D.: Toward a fully autonomous UAV: Research platform for indoor and outdoor urban search and rescue. IEEE Robot. Autom. Mag. 19(3), 46–56 (2012)

    Article  Google Scholar 

  2. Templeton, T., Shim, D.H., Geyer, C., Sastry, S. S.: Autonomous vision-based landing and terrain mapping using an MPC-controlled unmanned rotorcraft. In: IEEE International Conference on Robotics & Automation, pp. 1349-1356 (2007)

  3. Cesare, K., Skeele, R., Yoo, S.H., Zhang, Y., Hollinger, G.: Multi-UAV exploration with limited communication and battery. In: IEEE International Conference on Robotics & Automation, pp. 2230-2235 (2015)

  4. Stefano, P., Giorgio, G., Alessandro, R.: A risk-aware path planning strategy for UAVs in urban environments. J. Intell. Robot. Syst. 77(1), 229–246 (2015)

    Google Scholar 

  5. Pinto, S.C., Andersson, S.B., Hendrickx, J.M., Cassandras, C.G.: A semidefinite programming approach to discrete-time infinite horizon persistent monitoring. In: 2021 European Control Conference (ECC), pp. 799-804 (2021)

  6. Tokekar, P., Hook, J.V., Mulla, D., Isler, V.: Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Trans. Robot. 32(6), 1498–1511 (2016)

    Article  Google Scholar 

  7. Manyam, S. G., Rasmussen, S., Casbeer, D. W., Kalyanam, K., Manickam, S.: Multi-UAV routing for persistent intelligence surveillance & reconnaissance missions. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 573-580 (2017)

  8. Luitpold, B.: Coordinated target assignment and UAV path planning with timing constraints. J. Intell. Robot. Syst. 94(3–4), 857–869 (2019)

    Google Scholar 

  9. Wang, C., Liu, P., Zhang, T., Sun, J.: The adaptive vortex search algorithm of optimal path planning for forest fire rescue UAV. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 400-403 (2018)

  10. Jia, D., Wermelinger, M., Diethelm, R., Krusi, P., Hutter, M.: Coverage path planning for legged robots in unknown environments. In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 68-73 (2016)

  11. Salman, H., Ayvali, E., Choset, H.: Multi-agent ergodic coverage with obstacle avoidance. In: Twenty-seventh International Conference on Automated Planning & Scheduling, pp. 242-249 (2017)

  12. Cui, J.Q., Lai, S., Dong, X., Chen, B.M.: Autonomous navigation of UAV in foliage environment. J. Intell. Robot. Syst. 84(1–4), 259–276 (2015)

    Google Scholar 

  13. Hassan, M., Liu, D.: PPCPP: A predator-prey-based approach to adaptive coverage path planning. IEEE Trans. Robot. 36(1), 284–301 (2020)

    Article  Google Scholar 

  14. Xie, J., Garcia, C.L.R., Jin, L.: An integrated traveling salesman and coverage path planning problem for unmanned aircraft systems. IEEE Control Syst. Lett. 3(1), 67–72 (2019)

    Article  MathSciNet  Google Scholar 

  15. Hari, S. K. K Rathinam, S., Darbha, S., Kalyanam, K., Manyam, S. G., Casbeer, D.: Optimal UAV route planning for persistent monitoring missions. IEEE Trans. Robot. 37(2), 550-566 (2021)

  16. Maza, I., Ollero, A.: Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms. Distributed Autonomous Robotic Systems, 221-230 (2007)

  17. Paradzik, M., Ince, G.: Multi-agent search strategy based on digital pheromones for UAVs. In: 2016 24th Signal Processing and Communication Application Conference (SIU), pp. 233-236 (2016)

  18. Ivic, S., Crnkovic, B., Mezic, I.: Ergodicity-based cooperative multiagent area coverage via a potential field. IEEE Trans. Cybern. 47(8), 1983–1993 (2017)

    Article  Google Scholar 

  19. Borkar, A., Sinha, A., Vachhani, L., Arya, H.: Collision-free trajectory planning on lissajous curves for repeated multi-agent coverage and target detection. In: IEEE/RSJ International Conference on Intelligent Robots & Systems, pp. 1417-1422 (2016)

  20. Primatesta, S., Osman, A., Rizzo, A.: MP-\(RRT^{\sharp }\): a model predictive sampling-based motion planning algorithm for unmanned aircraft systems. J. Intell. Robot. Syst. 103(4), 1–13 (2021)

  21. Qin, H., Meng, Z., Meng, W., Chen, X., Sun, H., Lin, F., Ang, M.H.: Autonomous exploration and mapping system using heterogeneous UAVs and UGVs in GPS-denied environments. IEEE Trans. Veh. Technol. 68(2), 1339–1350 (2019)

    Article  Google Scholar 

  22. Garzon, M., Valente, J., Roldan, J.J., Cancar, L., Barrientos, A., Cerro, J.D.: A multirobot system for distributed area coverage and signal searching in large outdoor scenarios. J. Field Robot. 33(8), 1087–1106 (2016)

    Article  Google Scholar 

  23. Albani, D., Nardi, D., Trianni, V.: Field coverage and weed mapping by UAV swarms. In: IEEE/RSJ International Conference on Intelligent Robots & Systems, pp. 4319-4325 (2017)

  24. Peng, K., Tao, P., Lin, F., Chen, B. M.: Autonomous mission execution for multiple unmanned aerial vehicles with hierarchical-distributed methodology. In: IEEE International Conference on Control & Automation, pp. 1369-1374 (2014)

  25. Zhao, W., Meng, Q., Chung, P.W.H.: A heuristic distributed task allocation method for multivehicle multitask problems and its application to search and rescue scenario. IEEE Trans. Cybern. 46(4), 902–915 (2016)

    Article  Google Scholar 

  26. Asghar, A. B., Smith, S. L., Sundaram, S.: Multi-robot routing for persistent monitoring with latency constraints. In: 2019 American Control Conference (ACC), pp. 2620-2625 (2019)

  27. Peterson, C.K., Casbeer, D.W., Manyam, S.G., Rasmussen, S.: Persistent intelligence, surveillance, and reconnaissance using multiple autonomous vehicles with asynchronous route updates. IEEE Robot. Autom. Lett. 5(4), 5550–5557 (2020)

    Article  Google Scholar 

  28. Peng, K., Lin, F., Chen, B.M.: Online schedule for autonomy of multiple unmanned aerial vehicles. SCIENCE CHINA Inf. Sci. 60(7), 217–229 (2017)

    Article  Google Scholar 

  29. Huang, W. H.: Optimal line-sweep-based decompositions for coverage algorithms. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), vol. 1, pp. 27-32 (2001)

  30. Luo, Y., Huang, L., Zhong, H., C. G.: A secure protocol for determining whether a point is inside a convex polygon. Chinese Journal of Electronics 15(4), 578-582 (2006)

  31. Preparata, F. P., Shamos, M. I.: Computational Geometry: An Introduction. Springer-Verlay (1985)

  32. Wang, A., Zhao, G., Hou, F.: Constructing Bezier curves with monotone curvature. J. Comput. Appl. Math. 355, 1–10 (2019)

    Article  MathSciNet  Google Scholar 

  33. Zhou, Y., Hu, H., Liu, Y., Lin, S., Ding, Z.: A real-time and fully distributed approach to motion planning for multirobot systems. IEEE Trans. Syst. Man Cybern. Syst. 49(12), 2636–2650 (2019)

    Article  Google Scholar 

  34. Smith, A. J., G. A.: Hollinger, Distributed inference-based multi-robot exploration. Autonomous Robots 42, 1651-1668 (2018)

  35. Alotaibi, E.T., Alqefari, S.S., Koubaa, A.: LSAR: Multi-UAV collaboration for search and rescue missions. IEEE Access 7, 55817–55832 (2019)

    Article  Google Scholar 

  36. Meng, W., He, Z., Su, R., Xie, L.: Decentralized multi-UAV flight autonomy for moving convoys search and track. IEEE Trans. Control Syst. Technol. 25(4), 1480–1487 (2017)

    Article  Google Scholar 

  37. Hu, J., Xie, L., Lum, K.-Y., Xu, J.: Multiagent information fusion and cooperative control in target search. IEEE Trans. Control Syst. Technol. 21(4), 1223–1235 (2013)

    Article  Google Scholar 

  38. Zhang, L., Lei, S., Lu, Z., Zhang, X., Liu, J.: Path planning for the mobile robots in the environment with unknown obstacles. In: 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1153-1158 (2016)

  39. Hu, Y., Meng, W.: ROSUnitySim: Development and experimentation of a real-time simulator for multi-unmanned aerial vehicle local planning. Simulation 92(10), 931–944 (2016)

    Article  Google Scholar 

  40. Ahmed, Z.H.: Improved genetic algorithms for the travelling salesman problem. Int. J. Process. Manag. Benchmarking 4(1), 109–124 (2014)

    Article  Google Scholar 

  41. Tsiogkas, N., Saigol, Z., Lane, D.: Distributed multi-AUV cooperation methods for underwater archaeology. OCEANS 2015-Genova, May. 1-5 (2015)

  42. Changdar, C., Pal, R.K., Mahapatra, G.S.: A genetic ant colony optimization based algorithm for solid multiple travelling salesmen problem in fuzzy rough environment. Soft Comput. 21(16), 4661–4675 (2016)

    Article  Google Scholar 

  43. Kalec, B., Parlaktuna, O.: Performance analysis of bid calculation methods in multirobot market-based task allocation. Turk. J. Electr. Eng. Comput. Sci. 21(2), 565–585 (2013)

    Google Scholar 

  44. Wu, J. H., Qin, T. D., Chen, J., Si, H. P., Lin, K. Y., Zhou, Q.: Complete coverage path planning and obstacle avoidance strategy of the robot. Adv. Mat. Res. 346-351 (2012)

  45. Held, M., Lorenzo, S. D.: On the generation of spiral-like paths within planar shapes. J. Comput. Des. Eng. 348-357 (2018)

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Funding

This work was partially supported by the National Natural Science Foundation of China (61803105, 62121004, U1911401), Guangdong Introducing Innovative and Entrepreneurial Teams (2019ZT08X340), Guangdong Province Local Innovative and Research Teams Project of Guangdong Special Support Program (2019BT02X353) and the Key Area Research and Development Program of Guangdong Province (2021B0101410005).

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Authors and Affiliations

  1. School of Automation, Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, 510006, Guangzhou, China

    Xiaoling Xu, Damian Marelli, Wei Meng & Qianqian Cai

  2. French Argentine International Center for Information and Systems Sciences, National Scientific and Technical Research Council, Ocampo & Esmeralda, Rosario, Argentina

    Damian Marelli

  3. Sch Elect & Comp Engn, Georgia Inst Technol, 30332, Atlanta, GA, USA

    Fumin Zhang

  4. School of Engineering and Computer Science, University of Newcastle, 2308, Callaghan, NSW, Australia

    Minyue Fu

Authors
  1. Xiaoling Xu
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  2. Damian Marelli
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  3. Wei Meng
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  4. Fumin Zhang
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  5. Qianqian Cai
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Contributions

Xiaoling Xu: methodology, validation, visualization. Damian Marelli: methodology, visualization. Wei Meng: resources, visualization, supervision. Fumin Zhang: visualization. Qianqian Cai: validation. Minyue Fu: visualization, project administration

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Xu, X., Marelli, D., Meng, W. et al. Multi-MAV Autonomous Full Coverage Search in Cluttered Forest Environments. J Intell Robot Syst 106, 32 (2022). https://doi.org/10.1007/s10846-022-01723-z

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