“Quick Multi-Robot Motion Planning by Combining Sampling and Search.” IJCAI. 2023. In Press. • [Yu+ AAAI-13] Yu, J., & LaValle, S. “Structure and intractability of optimal multi-robot path planning on graphs.” AAAI. 2013. • [Ma+ AAAI-16] Ma.,H., Tovey, C., Sharon, G., Kumar, T. S., & Koenig, S. “Multi- agent path finding with payload transfers and the package-exchange robot-routing problem.” AAAI. 2016. • [Banfi+ RA-L-17] Banfi, J., Basilico, N., & Amigoni, F. “Intractability of time-optimal multirobot path planning on 2d grid graphs with holes.” RA-L. 2017. • [Hart+ 68] Hart, E. P., Nilsson, J. N. & Raphael, B. “A formal basis for the heuristic determination of minimum cost paths.” IEEE transactions on Systems Science and Cybernetics. 1968. • [Stern+ SOCS-19] Stern, R., et al. “Multi-agent pathfinding: Definitions, variants, and benchmarks.” SOCS. 2019. • [Shraon+ AIJ-15] Sharon, G., Stern, R., Felner, A., & Sturtevant, N. R. “Conflict-based search for optimal multi-agent pathfinding.” AIJ. 2015. • [Li+ AAAI-21] Li, J., Ruml, W., & Koenig, S. “EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding.” AAAI. 2021. • [Erdmann+ 87] Erdmann, M., & Lozano-Perez, T. “On multiple moving objects.” Algorithmica. 1987. • [Silver AIIDE-05] Silver, D. “Cooperative pathfinding.” AIIDE. 2005. • [Okumura+ AIJ-22] Okumura, K., Machida M., Défago, X. & Tamura, Y. “Priority Inheritance with Backtracking for Iterative Multi- agent Path Finding.” AIJ. 2022. • [Okumura AAAI-23] LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding. AAAI. 2023. • [Okumura IJCAI-23] Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding. IJCAI. 2023. In Press. • [Kavraki+ 96] Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. “Probabilistic roadmaps for path planning in high- dimensional configuration spaces.” IEEE transactions on Robotics and Automation. 1996. • [Okumura+ AAMAS-22] Okumura, K. et al. “CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces.” AAMAS. 2022. • [Salzmann+ ECCV-20] Salzmann, T., Ivanovic, B., Chakravarty, P., & Pavone, M. "Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data.” ECCV. 2020. 登場順 31