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This paper addresses the problem of autonomous UAV search missions, where a UAV must locate specific Entities of Interest (EOIs) within a time limit, based on brief descriptions in large, hazard-prone environments with keep-out zones.
P. Hart, N. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and Cybernetics , vol. 4, no. 2, pp. 100–107, 1968. [Online]. Available: https://doi.org/10.1109/tssc.1968.300136
1968
Earlier work this paper cites.
B. Oommen, S. Iyengar, N. Rao, and R. Kashyap, “Robot navigation in unknown terrains using learned visibility graphs. part i: The disjoint convex obstacle case,” IEEE Journal on Robotics and Automation , vol. 3, no. 6, pp. 672–681, 1987
1987
Earlier work this paper cites.
N. Nethercote, P. J. Stuckey, R. Becket, S. Brand, G. J. Duck, and G. Tack, “Minizinc: Towards a standard cp modelling language,” in Principles and Practice of Constraint Programming – CP 2007 , C. Bessière, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 529–543
2007
Earlier work this paper cites.
L. Blackmore, M. Ono, and B. C. Williams, “Chance-constrained optimal path planning with obstacles,” IEEE Transactions on Robotics , vol. 27, no. 6, pp. 1080–1094, 2011
2011
Earlier work this paper cites.
G. Chu, “Improving combinatorial optimization,” Ph.D. dissertation, University of Melbourne, Australia, 2011. [Online]. Available: http://hdl.handle.net/11343/36679
2011
Earlier work this paper cites.
L. Yang, J. Qi, J. Xiao, and X. Yong, “A literature review of uav 3d path planning,” in Proceeding of the 11th world congress on intelligent control and automation . IEEE, 2014, pp. 2376–2381
2014
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in Field and Service Robotics: Results of the 11th International Conference . Springer, 2018, pp. 621–635
2018
Earlier work this paper cites.
S. Primatesta, G. Guglieri, and A. Rizzo, “A risk-aware path planning strategy for uavs in urban environments,” Journal of Intelligent & Robotic Systems , vol. 95, 08 2019
2019
Earlier work this paper cites.
S. Macenski, F. Martín, R. White, and J. Ginés Clavero, “The marathon 2: A navigation system,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020. [Online]. Available: https://github.com/ros-planning/navigation2
2020
Earlier work this paper cites.
J. Huang, S. Xie, J. Sun, Q. Ma, C. Liu, D. Lin, and B. Zhou, “Learning a Decision Module by Imitating Driver’s Control Behaviors,” in Proceedings of the 2020 Conference on Robot Learning . PMLR, Oct. 2021, pp. 1–10, iSSN: 2640-3498. [Online]. Available: https://proceedings.mlr.press/v155/huang21a.html
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning Transferable Visual Models From Natural Language Supervision,” in Proceedings of the 38th International Conference on Machine Learning . PMLR, July 2021, pp. 8748–8763, iSSN: 2640-3498. [Online]. Available: https://proceedings.mlr.press/v139/radford21a.html
2021
Earlier work this paper cites.
J. Sun, H. Sun, T. Han, and B. Zhou, “Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design,” in Proceedings of the 2020 Conference on Robot Learning . PMLR, Oct. 2021, pp. 21–30, iSSN: 2640-3498. [Online]. Available: https://proceedings.mlr.press/v155/sun21a.html
2021
Earlier work this paper cites.
J. Duan, S. Yu, H. L. Tan, H. Zhu, and C. Tan, “A survey of embodied ai: From simulators to research tasks,” IEEE Transactions on Emerging Topics in Computational Intelligence , vol. 6, no. 2, pp. 230–244, 2022
2022
Cited alongside, same era.
L. H. Li, P. Zhang, H. Zhang, J. Yang, C. Li, Y. Zhong, L. Wang, L. Yuan, L. Zhang, J.-N. Hwang, et al. , “Grounded language-image pre-training,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 10 965–10 975
2022
Cited alongside, same era.
P. Mahmoudieh, D. Pathak, and T. Darrell, “Zero-Shot Reward Specification via Grounded Natural Language,” in Proceedings of the 39th International Conference on Machine Learning . PMLR, June 2022, pp. 14 743–14 752, iSSN: 2640-3498. [Online]. Available: https://proceedings.mlr.press/v162/mahmoudieh22a.html
2022
Cited alongside, same era.
——, “Fields2cover: An open-source coverage path planning library for unmanned agricultural vehicles,” IEEE Robotics and Automation Letters , vol. 8, no. 4, pp. 2166–2172, 2023
2023
Later among the works it cites.
N. D. Palo, A. Byravan, L. Hasenclever, M. Wulfmeier, N. Heess, and M. Riedmiller, “Towards A Unified Agent with Foundation Models,” in Workshop on Reincarnating Reinforcement Learning at ICLR 2023 , Mar. 2023. [Online]. Available: https://openreview.net/forum?id=JK_B1tB6p-
2023
Later among the works it cites.
D. Shah, B. Osiński, B. Ichter, and S. Levine, “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action,” in Proceedings of The 6th Conference on Robot Learning . PMLR, Mar. 2023, pp. 492–504, iSSN: 2640-3498. [Online]. Available: https://proceedings.mlr.press/v205/shah23b.html
2023
Later among the works it cites.
D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine, “ViNT: A Foundation Model for Visual Navigation,” in Proceedings of The 7th Conference on Robot Learning . PMLR, Dec. 2023, pp. 711–733, iSSN: 2640-3498. [Online]. Available: https://proceedings.mlr.press/v229/shah23a.html
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Y. Zeng, X. Zhang, and H. Li, “Multi-grained vision language pre-training: Aligning texts with visual concepts,” in International Conference on Machine Learning . PMLR, 2022, pp. 25 994–26 009
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Cited alongside, same era.
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J. Cao, J. Pang, X. Weng, R. Khirodkar, and K. Kitani, “Observation-centric sort: Rethinking sort for robust multi-object tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9686–9696
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2023
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