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Path Planning for Vehicle-borne System Consisting of Multi Air–ground Robots

Published online by Cambridge University Press:  17 June 2019

Yang Chen ,
Shiwen Ren ,
Zhihuan Chen ,
Mengqing Chen  and
Huaiyu Wu
Yang Chen*
Affiliation:
Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China. E-mails: snjrsw163.com, czh@wust.edu.cn, cm1803759221@163.com, wuhy@wust.edu.cn Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan 430081, China
Shiwen Ren
Affiliation:
Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China. E-mails: snjrsw163.com, czh@wust.edu.cn, cm1803759221@163.com, wuhy@wust.edu.cn Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan 430081, China
Zhihuan Chen
Affiliation:
Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China. E-mails: snjrsw163.com, czh@wust.edu.cn, cm1803759221@163.com, wuhy@wust.edu.cn Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan 430081, China
Mengqing Chen
Affiliation:
Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China. E-mails: snjrsw163.com, czh@wust.edu.cn, cm1803759221@163.com, wuhy@wust.edu.cn Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan 430081, China
Huaiyu Wu
Affiliation:
Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China. E-mails: snjrsw163.com, czh@wust.edu.cn, cm1803759221@163.com, wuhy@wust.edu.cn Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan 430081, China
*
*Corresponding author. E-mail: chenyag@wust.edu.cn
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Summary

This paper considers the path planning problem for deployment and collection of a marsupial vehicle system which consists of a ground mobile robot and two aerial flying robots. The ground mobile robot, usually unmanned ground vehicle (UGV), as a carrier, is able to deploy and harvest the aerial flying robots, and each aerial flying robot, usually unmanned aerial vehicles (UAVs), takes off from and lands on the carrier. At the same time, owing to the limited duration in the air in one flight, UAVs should return to the ground mobile robot timely for its energy-saving and recharge. This work is motivated by cooperative search and reconnaissance missions in the field of heterogeneous robot system. Especially, some targets with given positions are assumed to be visited by any of the UAVs. For the cooperative path planning problem, this paper establishes a mathematical model to solve the path of two UAVs and UGV. Many real constraints including the maximum speed of two UAVs and UGV, the minimum charging time of two UAVs, the maximum hovering time of UAVs, and the dynamic constraints among UAVs and UGV are considered. The objective function is constructed by minimizing the time for completing the whole mission. Finally, the path planning problem of the robot system is transformed into a multi-constrained optimization problem, and then the particle swarm optimization algorithm is used to obtain the path planning results. Simulations and comparisons verify the feasibility and effectiveness of the proposed method.

Keywords

Path planningDual UAVs systemAir–Ground cooperationPSO
Type
Articles
Information
Robotica , Volume 38 , Issue 3 , March 2020 , pp. 493 - 511
Copyright
© Cambridge University Press 2019 

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References

Yu, H., Meier, K., Argyle, M. and Beard, R. W., “Cooperative path planning for target tracking in urban environments using unmanned air and ground vehicles,” IEEE/ASME Trans. Mechatron. 20(2), 541–552 (2015).CrossRefGoogle Scholar
Manyam, S., Casbeer, D. and Sundar, K., “Path Planning for Cooperative Routing of Air-ground Vehicles,” American Control Conference, Piscataway, NJ, USA, IEEE (2016) pp. 4630–4635.Google Scholar
Azpúrua, H., Freitas, G. M., Macharet, D. G. and Campos, M. F., “Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys,” Robotica 36(8), 1144–1166 (2018).CrossRefGoogle Scholar
Liu, Y. and Bucknall, R., “A survey of formation control and motion planning of multiple unmanned vehicles,” Robotica 36(7), 1019–1047 (2018).CrossRefGoogle Scholar
Woosley, B. and Dasgupta, P., “Integrated real-time task and motion planning for multiple robots under path and communication uncertainties,” Robotica 36(3), 353–373 (2018).CrossRefGoogle Scholar
Benavidez, P. and Jamshidi, M., “Mobile Robot Navigation and Target Tracking System,” 2011 6th International Conference on System of Systems Engineering (SoSE), Piscataway, NJ, USA, IEEE (2011) pp. 299–304.CrossRefGoogle Scholar
Dong, P., Wang, X., Xing, H., Liu, Y. and Zhang, M., “Design and Control of a Tracked Robot for Search and Rescue in Nuclear Power Plant,” International Conference on Advanced Robotics and Mechatronics (ICARM), Piscataway, NJ, USA, IEEE (2016) pp. 330–335.CrossRefGoogle Scholar
Pineda, L., Takahashi, T., Jung, H. T., Zilberstein, S. and Grupen, R., “Continual Planning for Search and Rescue Robots,” 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), Piscataway, NJ, USA, IEEE (2015) pp. 243–248.CrossRefGoogle Scholar
Mathew, N., Smith, S. L. and Waslander, S. L., “Planning paths for package delivery in heterogeneous multirobot teams,” IEEE Trans. Automation Sci. Eng. 12(4), 1298–1308 (2015).CrossRefGoogle Scholar
Li, J., Deng, G., Luo, C., Lin, Q., Yan, Q. and Ming, Z., “A hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systems,” IEEE Trans. Vehicular Technol. 65(12), 9585–9596 (2016).CrossRefGoogle Scholar
Lau, G. and Liu, H. H. T., “Real-time path planning algorithm for autonomous border patrol: Design, simulation, and experimentation,” J. Intell. Robotic Syst. 75(3–4), 517–539 (2014).CrossRefGoogle Scholar
Cocchioni, F. and Mancini, A. S., “Longhi Autonomous Navigation, Landing and Recharge of a Quadrotor using Artificial Vision,” 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Piscataway, NJ, USA, IEEE (2014) pp. 418–429.CrossRefGoogle Scholar
Lee, D., Zhou, J. and Lin, W. T., “Autonomous Battery Swapping System for Quadcopter,” 2015 International Conference on Unmanned Aircraft Systems (ICUAS), Piscataway, NJ, USA, IEEE (2015) pp. 118–124.CrossRefGoogle Scholar
Hossain, M. A. and Ferdous, I., “Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique,” Robotics Autonomous Syst. 64, 137–141 (2015).CrossRefGoogle Scholar
Yu, J. and LaValle, S. M., “Planning Optimal Paths for Multiple Robots on Graphs,” 2013 IEEE International Conference on Robotics and Automation (ICRA), Piscataway, NJ, USA, IEEE (2013) pp. 3612–3617.CrossRefGoogle Scholar
Xiang, Y., Zhiwei, L., Zhipeng, W. and Xuanyu, C., “Dynamic Path Planning in RoboCup Rescue Simulation Competition,” 27th Chinese Control and Decision Conference (CCDC), Piscataway, NJ, USA, IEEE (2015) pp. 4341–4344.Google Scholar
Garone, E., Naldi, R., Casavola, A. and Frazzoli, E., “Cooperative Path Planning for a Class of Carrier-vehicle Systems,” 47th IEEE Conference on Decision and Control, Piscataway, NJ, USA, IEEE (2008) pp. 2456–2462.Google Scholar
Garone, E., Naldi, R., Casavola, A. and Frazzoli, E., “Cooperative Mission Planning for a Class of Carrier-vehicle Systems,” 49th IEEE Conference on Decision and Control (CDC), Piscataway, NJ, USA, IEEE (2010) pp. 1354–1359.CrossRefGoogle Scholar
Garone, E., Naldi, R., Casavola, A. and Frazzoli, E., “Planning algorithms for a class of heterogeneous multi-vehicle systems,” IFAC Proc. Vol. 43(14), 969–974 (2010).CrossRefGoogle Scholar
Garone, E., Naldi, R. and Casavola, A., “Traveling salesman problem for a class of carrier-vehicle systems,” J. Guidance Control Dyn. 34(4), 1272–1276 (2011).CrossRefGoogle Scholar
Mathew, N., Smith, S. L. and Waslander, S. L., “A Graph-based Approach to Multi-robot Rendezvous for Recharging in Persistent Tasks,” IEEE International Conference on Robotics and Automation (ICRA), Piscataway, NJ, USA, IEEE (2013) pp. 3497–3502.Google Scholar
Savuran, H. and Karakaya, M., “Efficient route planning for an unmanned air vehicle deployed on a moving carrier,” Soft Comput.20(7), 2905–2920 (2016).CrossRefGoogle Scholar
Maini, P. and Sujit, P. B., “On Cooperation between a Fuel Constrained UAV and a Refueling UGV for Large Scale Mapping Applications,” International Conference on Unmanned Aircraft Systems (ICUAS), Piscataway, NJ, USA, IEEE (2015) pp. 1370–1377.CrossRefGoogle Scholar
Yu, K., Budhiraja, A. K. and Tokekar, P., “Algorithms for Routing of Unmanned Aerial Vehicles with Mobile Recharging Stations and for Package Delivery,” IEEE International Conference on Robotics and Automation (ICRA), Piscataway, NJ, USA, IEEE (2018) pp. 1–5.Google Scholar
Liu, C. and Chen, W. H., “A practical receding horizon control framework for path planning and control of autonomous VTOL vehicles,” Progress Flight Dynamics Guidance Navigation Control Fault Detection Avionics 6, 159–174 (2013).CrossRefGoogle Scholar
Klauco, M., Blazek, S., Kvasnica, M. and Fikar, M., “Mixed-integer SOCP Formulation of the Path Planning Problem for Heterogeneous Multi-vehicle Systems,” 2014 European Control Conference (ECC), Piscataway, NJ, USA, IEEE (2014) pp. 1474–1479.CrossRefGoogle Scholar
Huisman, R., “Scheduling the Refuelling Activities of Multiple Heterogeneous Autonomous Mobile Robots”, Thesis (Delft University of Technology, 2014).Google Scholar
Murray, C. C. and Chu, A. G., “The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery,” Transport. Res. Part C: Emerging Technol. 54, 86–109 (2015).CrossRefGoogle Scholar
Luo, Z., and Liu, J. and Shi, J., “A two-echelon cooperated routing problem for a ground vehicle and its carried unmanned aerial vehicle,” Sensors 17(5), 1144 (2017).Google ScholarPubMed
Las Fargeas, J. C., Kabamba, P. T. and Girard, A. R., “Path Planning for Information Acquisition and Evasion Using Marsupial Vehicles,” American Control Conference (ACC), Piscataway, NJ, USA, IEEE (2015) pp. 3734–3739.CrossRefGoogle Scholar
Lee, H. S., Yoo, H. W. and Lee, B. H., “Deployment method of UAVs with energy constraint for multiple tasks,” Electronics Lett.51(21), 1650–1652 (2015).CrossRefGoogle Scholar
Chen, Y., Tan, Y., Cheng, L. and Wu, H., “Path planning for a heterogeneous aerial-ground robot system with neighborhood constraints,” Robot 39(1), 1–7 (2017). (In Chinese)Google Scholar
Ren, S., Chen, Y., Xiong, L., Chen, Z. and Chen, M., “Path Planning for the Marsupial Double-UAVs System in Air-ground Collaborative Application,” Proceedings of the 37th Chinese Control Conference, Piscataway, NJ, USA, IEEE (2018) pp. 5420–5425.Google Scholar
Wang, Y. R., Lin, H. L. and Yang, L., “Swarm Refinement PSO for Solving N-queens Problem,” Third International Conference on Innovations in Bio-inspired Computing & Applications, Piscataway, NJ, USA, IEEE (2012) pp. 29–33.Google Scholar
Aoki, T., Aranha, C. and Kanoh, H., “PSO Algorithm with Transition Probability Based on Hamming Distance for Graph Coloring Problem,” IEEE International Conference on Systems, Man, and Cybernetics, Piscataway, NJ, USA, IEEE (2015) pp. 1956–1961.CrossRefGoogle Scholar
Gionfra, N., Sandou, G., Siguerdidjane, H., Faille, D. and Loevenbruck, P., “Wind farm distributed PSO-based control for constrained power generation maximization,” Renew. Energ. 133, 103–117 (2019).CrossRefGoogle Scholar
Özgün-Kibiroğlu, Ç., Serarslan, M. N. and Topcu, Y. İ, “Particle swarm optimization for uncapacitated multiple allocation hub location problem under congestion,” Expert Syst. Appl. 119, 1–19 (2019).CrossRefGoogle Scholar
Das, P. K., Behera, H. S., Das, S., Tripathy, H. K., Panigrahi, B. K. and Pradhan, S. K., “A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment,” Neurocomputing 207, 735–753 (2016).CrossRefGoogle Scholar
Yan, Z., Li, J., Wu, Y. and Zhang, G., “A real-time path planning algorithm for AUV in unknown underwater environment based on combining PSO and waypoint Guidance,” Sensors 19, 20 (2019). doi: 10.3390/s19010020.CrossRefGoogle Scholar
Chen, Q., Lu, Y., Jia, G. W., Li, Y., Zhu, B. J. and Lin, J. C., “Path planning for UAVs formation reconfiguration based on Dubins trajectory,” J. Cent. South Univ. 25(11), 2664–2676 (2018).CrossRefGoogle Scholar
Huang, C. and Fei, J., “UAV path planning based on particle swarm optimization with global best path competition,” Int. J. Pattern Recognit. Artif. Intell. 32(6), 1859008 (2018).CrossRefGoogle Scholar
Oravec, J., Klauco, M., Kvasnica, M. and Löfberg, J., “Optimal Vehicle Routing with Interception of Targets’ Neighbourhoods,” 2015 European Control Conference (ECC), Piscataway, NJ, USA, IEEE (2015) pp. 2533–2538.CrossRefGoogle Scholar