[1] Steven M LaValle et al. Rapidly-exploring random trees: A new tool for path planning. 1998.
[2] Jonathan D Gammell, Timothy D Barfoot, and Siddhartha S Srinivasa. Batch informed trees (bit*): Informed asymptotically optimal anytime search. The International Journal of Robotics Research, 39(5):543-567, 2020.
[3] Peter E Hart, Nils J Nilsson, and Bertram Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics, 4(2):100-107, 1968.
[4] Edsger W Dijkstra. A note on two problems in connexion with graphs. In Edsger Wybe Dijkstra: His Life, Work, and Legacy, pages 287-290. 2022.
[5] Ahmed H Qureshi, Anthony Simeonov, Mayur J Bency, and Michael C Yip. Motion planning networks. In 2019 International Conference on Robotics and Automation (ICRA), pages 2118-2124. IEEE, 2019.
[6] Tomas Kulvicius, Sebastian Herzog, Timo Lüddecke, Minija Tamosiunaite, and Florentin Wörgotter. One-shot multi-path planning for robotic applications using fully convolutional networks. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 1460-1466. IEEE, 2020.
[7] Xiao Zang, Miao Yin, Lingyi Huang, Jingjin Yu, Saman Zonouz, and Bo Yuan. Robot motion planning as video prediction: A spatio-temporal neural network-based motion planner. arXiv preprint arXiv:2208.11287, 2022.
[8] Chenning Yu and Sicun Gao. Reducing collision checking for sampling-based motion planning using graph neural networks. Advances in Neural Information Processing Systems, 34:4274-4289, 2021.
[9] Arbaaz Khan, Alejandro Ribeiro, Vijay Kumar, and Anthony G Francis. Graph neural networks for motion planning. arXiv preprint arXiv:2006.06248, 2020.
[10] Charles E Leiserson and Tao B Schardl. A work-efficient parallel breadth-first search algorithm (or how to cope with the nondeterminism of reducers). In Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures, pages 303-314, 2010.
[11] Anthony Stentz et al. The focussed  $\mathsf{d}^{\wedge *}$  algorithm for real-time replanning. In IJCAI, volume 95, pages 1652-1659, 1995.
[12] Ira Pohl. Heuristic search viewed as path finding in a graph. Artificial intelligence, 1(3-4):193–204, 1970.
[13] Dmitri Dolgov, Sebastian Thrun, Michael Montemerlo, and James Diebel. Practical search techniques in path planning for autonomous driving. Ann Arbor, 1001(48105):18-80, 2008.
[14] Sertac Karaman and Emilio Frazzoli. Sampling-based algorithms for optimal motion planning. The international journal of robotics research, 30(7):846-894, 2011.
[15] Jonathan D Gammell, Siddhartha S Srinivasa, and Timothy D Barfoot. Informed rrt*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2997-3004. IEEE, 2014.
[16] Lydia E Kavraki, Petr Svestka, J-C Latombe, and Mark H Overmars. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE transactions on Robotics and Automation, 12(4):566-580, 1996.
[17] Nika Haghtalab, Simon Mackenzie, Ariel Procaccia, Oren Salzman, and Siddhartha Srinivasa. The provable virtue of laziness in motion planning. In Proceedings of the International Conference on Automated Planning and Scheduling, volume 28, pages 106-113, 2018.