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
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)
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)
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)
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)
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)
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)
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)
Luitpold, B.: Coordinated target assignment and UAV path planning with timing constraints. J. Intell. Robot. Syst. 94(3–4), 857–869 (2019)
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)
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)
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)
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)
Hassan, M., Liu, D.: PPCPP: A predator-prey-based approach to adaptive coverage path planning. IEEE Trans. Robot. 36(1), 284–301 (2020)
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)
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)
Maza, I., Ollero, A.: Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms. Distributed Autonomous Robotic Systems, 221-230 (2007)
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)
Ivic, S., Crnkovic, B., Mezic, I.: Ergodicity-based cooperative multiagent area coverage via a potential field. IEEE Trans. Cybern. 47(8), 1983–1993 (2017)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Preparata, F. P., Shamos, M. I.: Computational Geometry: An Introduction. Springer-Verlay (1985)
Wang, A., Zhao, G., Hou, F.: Constructing Bezier curves with monotone curvature. J. Comput. Appl. Math. 355, 1–10 (2019)
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)
Smith, A. J., G. A.: Hollinger, Distributed inference-based multi-robot exploration. Autonomous Robots 42, 1651-1668 (2018)
Alotaibi, E.T., Alqefari, S.S., Koubaa, A.: LSAR: Multi-UAV collaboration for search and rescue missions. IEEE Access 7, 55817–55832 (2019)
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)
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)
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)
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)
Ahmed, Z.H.: Improved genetic algorithms for the travelling salesman problem. Int. J. Process. Manag. Benchmarking 4(1), 109–124 (2014)
Tsiogkas, N., Saigol, Z., Lane, D.: Distributed multi-AUV cooperation methods for underwater archaeology. OCEANS 2015-Genova, May. 1-5 (2015)
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)
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)
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)
Held, M., Lorenzo, S. D.: On the generation of spiral-like paths within planar shapes. J. Comput. Des. Eng. 348-357 (2018)
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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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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DOI: https://doi.org/10.1007/s10846-022-01723-z