[1] 王鹤静, 王丽娜.机器人路径规划算法综述[J].桂林理工大学学报, 2023, 43(1): 137-147.
WANG H J, WANG L N. Review of path planning for robots[J]. Journal of Guilin University of Technology, 2023, 43(1): 137-147.
[2] DORIGO M, GAMBARDELLA L M. Ant colony system: a cooperative learning approach to the traveling salesman problem[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66.
[3] BREMERMANN H J. The evolution of intelligence: the nervous system as a model of its environment[D]. Washington: University of Washington. Department of Mathematics, 1958.
[4] 张硕航, 郭改枝.多旅行商模型及其应用研究综述[J].计算机科学与探索, 2022, 16(7): 1516-1528.
ZHANG S H, GUO G Z. Review of multiple traveling salesman model and its application[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(7): 1516-1528.
[5] 李珺, 段钰蓉, 郝丽艳, 等.混合优化算法求解同时送取货车辆路径问题[J].计算机科学与探索, 2022, 16(7): 1623-1632.
LI J, DUAN Y R, HAO L Y, et al. Hybrid optimization algorithm for vehicle routing problem with simultaneous delivery-pickup[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(7): 1623-1632.
[6] 肖金壮, 余雪乐, 周刚, 等.一种面向室内AGV路径规划的改进蚁群算法[J].仪器仪表学报, 2022, 43(3): 277-285.
XIAO J Z, YU X L, ZHOU G, et al. An improved ant colony algorithm for indoor AGV path planning[J]. Chinese Journal of Scientific Instrument, 2022, 43(3): 277-285.
[7] HART P E, NILSSON N J, RAPHAE B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE Transactions on Systems Science & Cybernetics, 1972, 4(2): 28-29.
[8] STENTZ A. Optimal and efficient path planning for partially known environments[M]//Intelligent unmanned ground vehicles. Boston: Springer, 1997: 203-220.
[9] LAVALLE S. Rapidly-exploring random trees: a new tool for path planning[R]. Iowa State University, 1998.
[10] KAVRAKI L E, SVESTKA P, LATOMBE J C, et al. Probabilistic roadmaps for path planning in high-dimensional configuration spaces[J]. IEEE Transactions on Robotics and Automation, 1996, 12(4): 566-580.
[11] KARAMAN S, FRAZZOLI E. Sampling-based algorithms for optimal motion planning[J]. The International Journal of Robotics Research, 2011, 30(7): 846-894.
[12] SALZMAN O, HALPERIN D. Asymptotically near-optimal RRT for fast, high-quality motion planning[J]. IEEE Transactions on Robotics, 2016, 32(3): 473-483.
[13] KUFFNER J J, LAVALLE S M. RRT-connect: an efficient approach to single-query path planning[C]//Proceedings of the 2002 IEEE International Conference on Robotics and Automation, 2002: 995-1001.
[14] 刘奥博, 袁杰.目标偏置双向RRT*算法的机器人路径规划[J].计算机工程与应用, 2022, 58(6): 234-240.
LIU A B, YUAN J. Robot path planning based on goal biased bidirectional RRT* algorithm[J]. Computer Engineering and Applications, 2022, 58(6): 234-240.
[15] JANSON L, SCHMERLING E, CLARK A, et al. Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions[J]. The International Journal of Robotics Research, 2015, 34(7): 883-921.
[16] GAMMELL J D, SRINIVASA S S, BARFOOT T D. Informed RRT: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic[C]//Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014: 2997-3004. |