[1] Fox D, Burgard W, Thrun S. The dynamic window approach to collision avoidance [J]. IEEE Robotics & Automation Magazine, 1997, 4(1): 23-33.
[2] Campbell S, Naeem W, Irwin GW. A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres [J]. Annual Reviews in Control, 2012, 36(2): 267-283.
[3] Casalino G, Turetta A, Simetti E. A three-layered architecture for real time path planning and obstacle avoidance for surveillance USVs operating in harbour fields [C]// Oceans. 2009: 1-8.
[4] Dijkstra E W. A note on two problems in connexion with graphs [J]. Numerische Mathematik, 1959, 1(1): 269-271.
[5] Hart P E, Nilsson N J, Raphael B. A formal basis for the heuristic determination of minimum cost paths [J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100-107.
[6] Kim H, Kim D, Shin J U, et al. Angular rate-constrained path planning algorithm for unmanned surface vehicles [J]. Ocean Engineering, 2014, 84: 37-44.
[7] Koenig S, Likhachev M, Furcy D. Lifelong planning A* [J]. Artificial Intelligence, 2004, 155(1): 93-146.
[8] Likhachev M, Ferguson D I, Gordon G J, et al. Anytime dynamic A*: an anytime, replanning algorithm [C]// Fifteenth International Conference on Automated Planning and Scheduling. 2005: 262-271.
[9] Likhachev M, Koenig S. A generalized framework for lifelong planning A* search [C]// Fifteenth International Conference on Automated Planning and Scheduling. 2005: 99-108.
[10] Stentz A. The focussed D* algorithm for real-time replanning [C]// International Joint Conference on Artificial Intelligence. 2000: 3310-3317.
[11] Botea A, Müler M, Schaeffer J. Near optimal hierarchical path-finding [J]. Journal of Game Development, 2004, 1(7): 7-28.
[12] Leigh R, Louis S J, Miles C. Using a genetic algorithm to explore A*-like pathfinding algorithms[C]// IEEE Symposium on Computational Intelligence and Games. 2007: 72-79.
[13] Yang S X, Luo C. A neural network approach to complete coverage path planning [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004, 34(1): 718-724.
[14] Melchior N A, Simmons R. Particle RRT for path planning with uncertainty [C]// IEEE International Conference on Robotics and Automation. 2007: 1617-1624.
[15] Hsu D, Kindel R, Latombe J C, et al. Randomized kinodynamic motion planning with moving obstacles [J]. International Journal of Robotics Research, 2002, 21(3): 233-255.
[16] Rodriguez, Tang X, Lien J M, et al. An obstacle-based rapidly-exploring random tree [C]//IEEE International Conference on Robotics and Automation. 2006: 895-900.
[17] Kamon I, Rivlin E, Rimon E. A new range-sensor based globally convergent navigation algorithm for mobile robots [C]// IEEE International Conference on Robotics & Automation. 1995: 429-435.
[18] Ng J, Bränl T. Performance comparison of bug navigation algorithms [J]. Journal of Intelligent and Robotic Systems, 2007, 50(1): 73-84.
[19] Kamon I, Rimon E, Rivlin E. TangentBug: a range-sensor-based navigation algorithm [J]. International Journal of Robotics Research, 1998, 17(9): 934-953.
[20] Maravall D, Lope J D, Fuentes J P. Visual bug algorithm for simultaneous robot homing and obstacle avoidance using visual topological maps in an unmanned ground vehicle [M]. New York: Springer International Publishing, 2015: 301-310.
[21] Zohaib M, Pasha S M, Javaid N, et al. Intelligent bug algorithm (IBA): a novel strategy to navigate mobile robots autonomously [C]// Springers International Multi Topic Conference. 2013.
[22] Hashmi U, Afshan F, Rafiq M. Performance analysis of different optimal path planning bug algorithms on a client server based mobile surveillance UGV [C]// International Conference on Intelligent Systems Modelling & Simulation. 2013: 30-35.
[23] Buniyamin N, Wan N W, Sariff N, et al. A simple local path planning algorithm for autonomous mobile robots [J]. International Journal of Systems Applications, Engineering & Development, 2011, 5(2): 151-159.
[24] Mohamed E F, ElMetwally K, Hanafy A. An improved Tangent Bug method integrated with artificial potential field for multi-robot path planning [C]// International Symposium on Innovations in Intelligent Systems and Applications. 2011: 555-559.
[25] Khatib M, Chatila R. An extended potential field approach for mobile robot sensor-based motions [C]// Intelligent Autonomous Systems. 1995: 490-496.
[26] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots [J]. International Journal of Robotics Research, 1986, 5(1): 90-98.
[27] Huang Y, Hu H, Liu X. Obstacles avoidance of artificial potential field method with memory function in complex environment [C]// Intelligent Control and Automation. 2010: 6414-6418.
[28] Zohaib M, Pasha S M, Javaid N, et al. An improved algorithm for collision avoidance in environments having U and H shaped obstacles [J]. Studies in Informatics & Control, 2014, 23(1): 97-106.
[29] Sezer V, Gokasan M. A novel obstacle avoidance algorithm: “follow the gap method” [J]. Robotics & Autonomous Systems, 2012, 60(9): 1123-1134.
[30] Brock O, Khatib O. High-speed navigation using the global dynamic window approach [C]//IEEE International Conference on Robotics and Automation. 1999: 341-346.
[31] Seder M, Petrovic I. Dynamic window based approach to mobile robot motion control in the presence of moving obstacles [C]// IEEE International Conference on Robotics and Automation. 2007: 1986-1991.
[32] Tang P, Zhang R, Liu D, et al. Research on near-field obstacle avoidance for unmanned surface vehicle based on heading window [C]// 24th Chinese Control and Decision Conference. 2012: 1262-1267.
[33] Zhang R, Tang P, Su Y, et al. An adaptive obstacle avoidance algorithm for unmanned surface vehicle in complicated marine environments [J]. IEEE/CAA Journal of Automatica Sinica, 2014, 1(4): 385-396.
[34] Simetti E, Torelli S, Casalino G, et al. Experimental results on obstacle avoidance for high speed unmanned surface vehicles [C]// Oceans. 2014: 1-6.
[35] Wu G, Shi D, Guo J. Deliberative collision avoidance for unmanned surface vehicle based on the directional weight [J]. Journal of Shanghai Jiaotong University (Science), 2016, 21(3): 307-312.
[36] Chakravarthy A, Ghose D. Obstacle avoidance in a dynamic environment: a collision cone approach [J]. Systems & Humans, 1998, 28(5): 562-574.
[37] Chakravarthy A, Ghose D. Collision cones for quadric surfaces [J]. IEEE Transactions on Robotics, 2011, 27(6): 1159-1166.
[38] Fiorini P. Motion planning in dynamic environments using velocity obstacles [J]. International Journal of Robotics Research, 1998, 17(7): 760-772.
[39] Kuwata Y, Wolf M T, Zarzhitsky D, et al. Safe maritime autonomous navigation with colregs, using velocity obstacles [J]. IEEE Journal of Oceanic Engineering, 2014, 39(1): 110-119.
[40] Johansen T A, Perez T, Cristofaro A. Ship collision avoidance and colregs compliance using simulation-based control behavior selection with predictive hazard assessment [J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17: 1-16.
[41] Naeem W, Irwin G W, Yang A. COLREGs-based collision avoidance strategies for unmanned surface vehicles [J]. Mechatronics, 2012, 22(6): 669-678.
[42] Breivik M, Hovstein V, Fossen T. Straight-line target tracking for unmanned surface vehicles [J]. Modeling Identification and Control, 2008, 29(4): 131-149.
[43] Breivik M, Fossen T I. Guidance laws for planar motion control [C]// IEEE Conference on Decision and Control. 2008: 570-577. |