Path planning of multiple autonomous marine vehicles for adaptive sampling using Voronoi-based ant colony optimization

Robotics and Autonomous Systems - Tập 115 - Trang 90-103 - 2019
Chengke Xiong1,2, Danfeng Chen1,2, Di Lu1,2, Zheng Zeng1,2,3, Lian Lian1,2,3
1State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, China
2Institute of Oceanography, Shanghai Jiao Tong University, Shanghai, China
3Qingdao Collaborative Innovation Center of Marine Science and Technology, Qingdao, China

Tài liệu tham khảo

Das, 2016, Cooperative formation control of autonomous underwater vehicles: An overview, Int. J. Automat. Comput., 13, 199, 10.1007/s11633-016-1004-4 Rudnick, 2016, Ocean research enabled by underwater gliders, Ann. Rev. Mar. Sci., 8, 519, 10.1146/annurev-marine-122414-033913 Nad, 2015, Navigation, guidance and control of an overactuated marine surface vehicle, Annu. Rev. Control, 40, 172, 10.1016/j.arcontrol.2015.08.005 Zeng, 2015, A survey on path planning for persistent autonomy of autonomous underwater vehicles, Ocean Eng., 110, 303, 10.1016/j.oceaneng.2015.10.007 Liu, 2015, Unmanned surface vehicles: An overview of developments and challenges, Annu. Rev. Control, 41, 71, 10.1016/j.arcontrol.2016.04.018 Ryan, 2010, USC CINAPS Builds bridges observing and monitoring the southern california, IEEE Robot. Autom. Mag., 17, 20, 10.1109/MRA.2010.935795 Mahmoudzadeh, 2018, UUV’s hierarchical DE-based motion planning in a semi dynamic underwater wireless sensor network, IEEE Trans. Cybern., 1, 10.1109/TCYB.2018.2837134 Subramani, 2016, Energy-optimal path planning by stochastic dynamically orthogonal level-set optimization, Ocean Model., 100, 57, 10.1016/j.ocemod.2016.01.006 Zeng, 2016, A comparison of optimization techniques for AUV path planning in environments with ocean currents, Robot. Auton. Syst., 82, 61, 10.1016/j.robot.2016.03.011 Eichhorn, 2015, Optimal routing strategies for autonomous underwater vehicles in time-varying environment, Robot. Auton. Syst., 67, 33, 10.1016/j.robot.2013.08.010 Garau, 2005, Path planning of autonomous underwater vehicles in current fields with complex spatial variability: an a* approach, 194 Hollinger, 2014, Sampling-based robotic information gathering algorithms, Int. J. Robot. Res., 33, 1271, 10.1177/0278364914533443 Yilmaz, 2008, Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming, IEEE J. Ocean. Eng., 33, 522, 10.1109/JOE.2008.2002105 Smith, 2011, Persistent ocean monitoring with underwater gliders: Adapting sampling resolution, J. Field Robot., 28, 714, 10.1002/rob.20405 Ferri, 2015, Mission planning and decision support for underwater glider networks: A sampling on-demand approach, Sensors (Switzerland), 16, 10.3390/s16010028 Das, 2015, Data-driven robotic sampling for marine ecosystem monitoring, Int. J. Robot. Res., 34, 1435, 10.1177/0278364915587723 Ma, 2017, Data-driven learning and planning for environmental sampling, J. Field Robot. Ma, 2016, An information-driven and disturbance-aware planning method for long-term ocean monitoring, 2102 Zhou, 2017, Adaptive re-planning of AUVs for environmental sampling missions: A Fuzzy decision support system based on multi-objective particle swarm optimization, Int. J. Fuzzy Syst. Zeng, 2018, Rendezvous path planning for multiple autonomous marine vehicles, IEEE J. Ocean. Eng., 43, 640, 10.1109/JOE.2017.2723058 Moon, 2015, Decentralized information-theoretic task assignment for searching and tracking of moving targets, 1031 Kovács, 2016, A novel potential field method for path planning of mobile robots by adapting animal motion attributes, Robot. Auton. Syst., 82, 24, 10.1016/j.robot.2016.04.007 Dijkstra, 1959, A note on two problems in connection with graphs, 269 Fu, 2018, An improved a* algorithm for the industrial robot path planning with high success rate and short length, Robot. Auton. Syst., 106, 26, 10.1016/j.robot.2018.04.007 Ferguson, 2006, Using interpolation to improve path planning : The field D * algorithm, J. Field Robot., 23, 79, 10.1002/rob.20109 Karaman, 2010, Sampling-based algorithms for optimal motion planning, Int. J. Robot. Res., 30, 20 Elbanhawi, 2014, Sampling-based robot motion planning: A review, IEEE Access, 2, 56, 10.1109/ACCESS.2014.2302442 Aghababa, 2012, Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles, J. Mar. Sci. Appl., 11, 378, 10.1007/s11804-012-1146-x Zeng, 2014, Shell space decomposition based path planning for AUVs operating in a variable environment, Ocean Eng., 91, 181, 10.1016/j.oceaneng.2014.09.001 Blum, 2005, Ant colony optimization: Introduction and recent trends, Phys. Life Rev., 2, 353, 10.1016/j.plrev.2005.10.001 Marinakis, 2017, A hybrid particle swarm optimization variable neighborhood search algorithm for constrained shortest path problems, European J. Oper. Res., 261, 819, 10.1016/j.ejor.2017.03.031 YongBo, 2017, Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm, Neurocomputing, 266, 445, 10.1016/j.neucom.2017.05.059 Ma, 1995, Parameterization of randomly measured points for least squares fitting of b-spline curves and surfaces, Comput. Aided Des., 27, 663, 10.1016/0010-4485(94)00018-9 Arzamendia, 2017, An evolutionary approach to constrained path planning of an autonomous surface vehicle for maximizing the covered area of Ypacarai Lake, Soft Comput., 1 Dorigo, 1996, Ant system: Optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybern., 26, 1, 10.1109/3477.484436 Cui, 2016, Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT*, IEEE Trans. Syst. Man Cybern. Syst., 46, 993, 10.1109/TSMC.2015.2500027 Ammar, 2016, Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments, Soft Comput., 20, 4149, 10.1007/s00500-015-1750-1 Perez-Carabaza, 2018, Ant colony optimization for multi-UAV minimum time search in uncertain domains, Appl. Soft Comput. J., 62, 789, 10.1016/j.asoc.2017.09.009 MahmoudZadeh, 2018, Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms, Appl. Soft Comput., 70, 929, 10.1016/j.asoc.2017.10.025 Zeng, 2015, Efficient path re-planning for AUVs operating in spatiotemporal currents, J. Intell. Robot. Syst., Theory Appl., 79, 135, 10.1007/s10846-014-0104-z Yazdani, 2017, IDVD-based trajectory generator for autonomous underwater docking operations, Robot. Auton. Syst., 92, 12, 10.1016/j.robot.2017.02.001 Cao, 2016, Toward optimal rendezvous of multiple underwater gliders: 3D path planning with combined sawtooth and spiral motion, J. Intell. Robot. Syst., Theory Appl., 1 Cao, 2016, Nonlinear multiple-input-multiple-output adaptive backstepping control of underwater glider systems, Int. J. Adv. Robot. Syst., 13, 1, 10.1177/1729881416669484 Yu, 2017, Nonlinear guidance and fuzzy control for three-dimensional path following of an underactuated autonomous underwater vehicle, Ocean Eng., 146, 457, 10.1016/j.oceaneng.2017.10.001 Xiang, 2017, On intelligent risk analysis and critical decision of underwater robotic vehicle, Ocean Eng., 140, 453, 10.1016/j.oceaneng.2017.06.020