Fetching the paper…
Reading the bibliography…
Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects.
E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische mathematik , vol. 1, no. 1, pp. 269–271, 1959
1959
Earlier work this paper cites.
A. Stentz, “Optimal and efficient path planning for partially known environments,” in IEEE International Conference on Robotics and Automation , 1994, pp. 3310–3317
1994
Earlier work this paper cites.
O. Brock and O. Khatib, “High-speed navigation using the global dynamic window approach,” in IEEE International Conference on Robotics and Automation , 1999, pp. 341–346
1999
Earlier work this paper cites.
L. C. Wang, L. S. Yong, and M. H. A. Jr, “Hybrid of global path planning and local navigation implemented on a mobile robot in indoor environment,” in IEEE Internatinal Symposium on Intelligent Control , 2002, pp. 821–826
2002
Earlier work this paper cites.
R. Smierzchalski and Z. Michalewicz, Path Planning in Dynamic Environments . Springer Berlin Heidelberg, 2005
2005
Earlier work this paper cites.
D. Silver, “Cooperative path-finding,” in AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment , 2005, pp. 117–122
2005
Earlier work this paper cites.
M. Phillips and M. Likhachev, “Sipp: Safe interval path planning for dynamic environments,” in IEEE International Conference on Robotics and Automation , 2011, pp. 5628–5635
2011
Earlier work this paper cites.
P. Li, X. Huang, and M. Wang, “A novel hybrid method for mobile robot path planning in unknown dynamic environment based on hybrid dsm model grid map,” Journal of Experimental & Theoretical Artificial Intelligence , vol. 23, no. 1, pp. 5–22, 2011
2011
Earlier work this paper cites.
J. Van Den Berg, S. J. Guy, M. Lin, and D. Manocha, “Reciprocal n-body collision avoidance,” in Robotics Research , 2011, pp. 3–19
2011
Earlier work this paper cites.
M. Barer, G. Sharon, R. Stern, and A. Felner, “Sub-optimal variants of the conflict-based search algorithm for the multi-agent path-finding problem,” in European Conference on Artificial Intelligence , 2014, pp. 961–962
2014
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
B. Ichter, J. Harrison, and M. Pavone, “Learning sampling distributions for robot motion planning,” in IEEE International Conference on Robotics and Automation , 2018, pp. 7087–7094
2018
Later among the works it cites.
G. Sartoretti, J. Kerr, Y. Shi, G. Wagner, T. S. Kumar, S. Koenig, and H. Choset, “Primal: Pathfinding via reinforcement and imitation multi-agent learning,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 2378–2385, 2019
2019
Later among the works it cites.
X. Pan, W. Wang, X. Zhang, B. Li, J. Yi, and D. Song, “How you act tells a lot: Privacy-leaking attack on deep reinforcement learning,” in International Conference on Autonomous Agents and Multi-Agent Systems , 2019, pp. 368–376
2019
Later among the works it cites.
Z. Liu, H. Wang, H. Wei, M. Liu, and Y.-H. Liu, “Prediction, planning and coordination of thousand-warehousing-robot networks with motion and communication uncertainties,” IEEE Transactions on Automation Science and Engineering , 2020
2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Mehta, G. Ferrer, and E. Olson, “Autonomous navigation in dynamic social environments using multi-policy decision making,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2016, pp. 1190–1197
2016
Cited alongside, same era.
D. Silver and et. al., “Mastering the game of go without human knowledge,” Nature , vol. 550, no. 7676, pp. 354–359, 2017
2017
Cited alongside, same era.
M. Bhardwaj, S. Choudhury, and S. Scherer, “Learning heuristic search via imitation,” in Conference on Robot Learning , 2017, pp. 271–280
2017
Cited alongside, same era.
L. Cohen, T. Uras, S. Jahangiri, A. Arunasalam, S. Koenig, and T. K. S. Kumar, “The fastmap algorithm for shortest path computations,” in International Joint Conference on Artificial Intelligence , 2018, pp. 1427–1433
2018
Cited alongside, same era.
R. S. Sutton and A. G. Barto, Introduction to Reinforcement Learning . MIT Press, 2018
2018
Cited alongside, same era.
Closest in time.
Q. Li, F. Gama, A. Ribeiro, and A. Prorok, “Graph neural networks for decentralized multi-robot path planning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
H. V. Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning,” in AAAI Conference on Artificial Intelligence , 2016, pp. 2094–2100
2094
Closest in time.