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张逍怡 等: 面向智能体路径规划算法的动态随机测试方法                                                     3131


                     Int’l Conf. on Automated Software Engineering (ASE). Melbourne: IEEE, 2021. 1171–1173. [doi: 10.1109/ASE51524.2021.9678841]
                 [11]  Schmidt T, Pretschner A. StellaUAV: A tool for testing the safe behavior of UAVs with scenario-based testing (tools and artifact track).
                     In:  Proc.  of  the  33rd  IEEE  Int’l  Symp.  on  Software  Reliability  Engineering  (ISSRE).  Charlotte:  IEEE,  2022.  37–48.  [doi:  10.1109/
                     ISSRE55969.2022.00015]
                 [12]  Liu Y, Zheng Z, Qin FY. Homotopy based optimal configuration space reduction for anytime robotic motion planning. Chinese Journal of
                     Aeronautics, 2021, 34(1): 364–379. [doi: 10.1016/j.cja.2020.09.036]
                 [13]  Zhang JT, Zheng Z, Yin BB, Qiu K, Liu Y. Testing graph searching based path planning algorithms by metamorphic testing. In: Proc. of
                     the 24th IEEE Pacific Rim Int’l Symp. on Dependable Computing. Kyoto: IEEE, 2019. 158–167. [doi: 10.1109/PRDC47002.2019.00046]
                 [14]  Arcuri A, Iqbal MZ, Briand L. Random testing: Theoretical results and practical implications. IEEE Trans. on Software Engineering,
                     2012, 38(2): 258–277. [doi: 10.1109/TSE.2011.121]
                 [15]  Gammell JD, Barfoot TD, Srinivasa SS. Batch informed trees (BIT*): Informed asymptotically optimal anytime search. The Int’l Journal
                     of Robotics Research, 2020, 39(5): 543–567. [doi: 10.1177/0278364919890396]
                 [16]  Karaman S, Walter MR, Perez A, Frazzoli E, Teller S. Anytime motion planning using the RRT*. In: Proc. of the 2011 IEEE Int’l Conf.
                     on Robotics and Automation. Shanghai: IEEE, 2011. 1478–1483. [doi: 10.1109/ICRA.2011.5980479]
                 [17]  Solovey K, Janson L, Schmerling E, Frazzoli E, Pavone M. Revisiting the asymptotic optimality of RRT. In: Proc. of the 2020 IEEE Int’l
                     Conf. on Robotics and Automation (ICRA). Paris: IEEE, 2020. 2189–2195. [doi: 10.1109/ICRA40945.2020.9196553]
                 [18]  Pei HY, Yin BB, Xie M, Cai KY. Dynamic random testing with test case clustering and distance-based parameter adjustment. Information
                     and Software Technology, 2021, 131: 106470. [doi: 10.1016/j.infsof.2020.106470]
                 [19]  Zhang XY, Liu Y, Arcaini P, Jiang MY, Zheng Z. MET-MAPF: A metamorphic testing approach for multi-agent path finding algorithms.
                     ACM Trans. on Software Engineering and Methodology, 2024, 33(8): 1–37. [doi: 10.1145/3669663]
                 [20]  Zhang XD, Cai Y. Building critical testing scenarios for autonomous driving from real accidents. In: Proc. of the 32nd ACM SIGSOFT
                     Int’l Symp. on Software Testing and Analysis. Seattle: ACM, 2023. 462–474. [doi: 10.1145/3597926.3598070]
                 [21]  Singh S, Padhi R. Automatic path planning and control design for autonomous landing of UAVs using dynamic inversion. In: Proc. of the
                     2009 American Control Conf. St. Louis: IEEE, 2009. 2409–2414. [doi: 10.1109/ACC.2009.5160444]
                 [22]  Okumura K, Machida M, Défago X, Tamura Y. Priority inheritance with backtracking for iterative multi-agent path finding. Artificial
                     Intelligence, 2022, 310: 103752. [doi: 10.1016/j.artint.2022.103752]
                 [23]  Lin HI, Hsieh MF. Robotic arm path planning based on three-dimensional artificial potential field. In: Proc. of the 18th Int’l Conf. on
                     Control, Automation and Systems (ICCAS). Pyeongchang: IEEE, 2018. 740–745.
                 [24]  Zhang B, Tang L, Roemer M. Probabilistic weather forecasting analysis for unmanned aerial vehicle path planning. Journal of Guidance,
                     Control, and Dynamics, 2014, 37(1): 309–312. [doi: 10.2514/1.61651]
                 [25]  Arcaini P, Zhang XY, Ishikawa F. Targeting patterns of driving characteristics in testing autonomous driving systems. In: Proc. of the
                     14th  IEEE  Conf.  on  Software  Testing,  Verification  and  Validation  (ICST).  Porto  de  Galinhas:  IEEE,  2021.  295–305.  [doi:  10.1109/
                     ICST49551.2021.00042]
                 [26]  Wang CB, Wang L, Qin J, Wu ZZ, Duan LH, Li ZQ, Cao MQ, Ou XC, Su X, Li WG, Lu ZJ, Li MJ, Wang YL, Long JJ, Huang ML, Li
                     YH, Wang QH. Path planning of automated guided vehicles based on improved A-Star algorithm. In: Proc. of the 2015 IEEE Int’l Conf.
                     on Information and Automation. Lijiang: IEEE, 2015. 2071–2076. [doi: 10.1109/ICInfA.2015.7279630]
                 [27]  Hu Y, Harabor D, Qin L, Yin QJ. Regarding goal bounding and jump point search. Journal of Artificial Intelligence Research, 2021, 70:
                     631–681. [doi: 10.1613/jair.1.12255]
                 [28]  Reif  JH.  Complexity  of  the  mover’s  problem  and  generalizations.  In:  Proc.  of  the  20th  Annual  Symp.  on  Foundations  of  Computer
                     Science. San Juan: IEEE, 1979. 421–427. [doi: 10.1109/SFCS.1979.10]
                 [29]  Hu YR, Yang SX. A knowledge based genetic algorithm for path planning of a mobile robot. In: Proc. of the 2004 IEEE Int’l Conf. on
                     Robotics and Automation, 2004. New Orleans: IEEE, 2004. 4350–4355. [doi: 10.1109/ROBOT.2004.1302402]
                 [30]  Huang  C,  Zhou  XB,  Ran  XJ,  Wang  JM,  Chen  HY,  Deng  W.  Adaptive  cylinder  vector  particle  swarm  optimization  with  differential
                     evolution for UAV path planning. Engineering Applications of Artificial Intelligence, 2023, 121: 105942. [doi: 10.1016/j.engappai.2023.
                     105942]
                 [31]  Taylor B, Choi A. Fuzzy ant colony algorithm for terrain following optimization. In: Proc. of the 2014 IEEE Int’l Conf. on Systems, Man,
                     and Cybernetics (SMC). San Diego: IEEE, 2014. 3834–3839. [doi: 10.1109/SMC.2014.6974528]
                 [32]  Penrose M. Random Geometric Graphs. Oxford: Oxford University Press, 2003.
                 [33]  Chandler B, Goodrich MA. Online RRT* and online FMT*: Rapid replanning with dynamic cost. In: Proc. of the 2017 IEEE/RSJ Int’l
                     Conf. on Intelligent Robots and Systems (IROS). Vancouver: IEEE, 2017. 6313–6318. [doi: 10.1109/IROS.2017.8206535]
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