Fetching the paper…
Reading the bibliography…
This paper presents a novel learning-based trajectory planning framework for quadrotors that combines model-based optimization techniques with deep learning.
J. Bobrow, S. Dubowsky, and J. Gibson, “Time-optimal control of robotic manipulators along specified paths,” The International Journal of Robotics Research , vol. 4, no. 3, pp. 3–17, 1985. [Online]. Available: https://doi.org/10.1177/027836498500400301
1985
Earlier work this paper cites.
D. C. Liu and J. Nocedal, “On the limited memory bfgs method for large scale optimization,” Mathematical programming , vol. 45, no. 1-3, pp. 503–528, 1989
1989
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex optimization . Cambridge university press, 2004
2004
Earlier work this paper cites.
S. G. Johnson, “The NLopt nonlinear-optimization package,” https://github.com/stevengj/nlopt , 2007
2007
Earlier work this paper cites.
D. Mellinger and V. Kumar, “Minimum snap trajectory generation and control for quadrotors,” in 2011 IEEE International Conference on Robotics and Automation , 2011, pp. 2520–2525
2011
Earlier work this paper cites.
S. Ross, N. Melik-Barkhudarov, K. S. Shankar, A. Wendel, D. Dey, J. A. Bagnell, and M. Hebert, “Learning monocular reactive uav control in cluttered natural environments,” in 2013 IEEE International Conference on Robotics and Automation , 2013, pp. 1765–1772
2013
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 , ser. NIPS’14. Cambridge, MA, USA: MIT Press, 2014, p. 3104–3112
2014
Earlier work this paper cites.
J. D. Gammell, S. S. Srinivasa, and T. 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 , 2014, pp. 2997–3004
2014
Earlier work this paper cites.
A. Bry, C. Richter, A. Bachrach, and N. Roy, “Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments,” The International Journal of Robotics Research , vol. 34, no. 7, pp. 969–1002, 2015. [Online]. Available: https://doi.org/10.1177/0278364914558129
2015
Earlier work this paper cites.
M. Burri, H. Oleynikova, M. W. Achtelik, and R. Siegwart, “Real-time visual-inertial mapping, re-localization and planning onboard mavs in unknown environments,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2015, pp. 1872–1878
2015
Earlier work this paper cites.
J. Chen, T. Liu, and S. Shen, “Online generation of collision-free trajectories for quadrotor flight in unknown cluttered environments,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) , 2016, pp. 1476–1483
2016
Earlier work this paper cites.
C. Richter, A. Bry, and N. Roy, “Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments,” in Robotics Research: The 16th International Symposium ISRR . Springer, 2016, pp. 649–666
2016
Earlier work this paper cites.
S. Liu, M. Watterson, K. Mohta, K. Sun, S. Bhattacharya, C. J. Taylor, and V. Kumar, “Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-d complex environments,” IEEE Robotics and Automation Letters , vol. 2, no. 3, pp. 1688–1695, 2017
2017
Earlier work this paper cites.
B. Amos and J. Z. Kolter, “Optnet: Differentiable optimization as a layer in neural networks,” in Proceedings of the 34th International Conference on Machine Learning - Volume 70 , ser. ICML’17. JMLR.org, 2017, p. 136–145
2017
Cited alongside, same era.
M. M. de Almeida and M. Akella, “New numerically stable solutions for minimum-snap quadcopter aggressive maneuvers,” in 2017 American Control Conference (ACC) , 2017, pp. 1322–1327
2017
Cited alongside, same era.
S. Liu, N. Atanasov, K. Mohta, and V. Kumar, “Search-based motion planning for quadrotors using linear quadratic minimum time control,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2017, pp. 2872–2879
2017
Cited alongside, same era.
F. Gao, W. Wu, J. Pan, B. Zhou, and S. Shen, “Optimal time allocation for quadrotor trajectory generation,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 4715–4722
2018
Cited alongside, same era.
A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza, “Learning high-speed flight in the wild,” Science Robotics , vol. 6, no. 59, p. eabg5810, 2021. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.abg5810
2021
Later among the works it cites.
J. Tordesillas and J. P. How, “FASTER: Fast and safe trajectory planner for navigation in unknown environments,” IEEE Transactions on Robotics , 2021
2021
Later among the works it cites.
P. Foehn, A. Romero, and D. Scaramuzza, “Time-optimal planning for quadrotor waypoint flight,” Science Robotics , vol. 6, no. 56, p. eabh1221, 2021. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.abh1221
2021
Later among the works it cites.
P. Donti, D. Rolnick, and J. Z. Kolter, “Dc3: A learning method for optimization with hard constraints,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
G. Tang, W. Sun, and K. Hauser, “Learning trajectories for real- time optimal control of quadrotors,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 3620–3625
2018
Cited alongside, same era.
A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Z. Kolter, “Differentiable convex optimization layers,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
I. Spasojevic, V. Murali, and S. Karaman, “Perception-aware time optimal path parameterization for quadrotors,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 3213–3219
2020
Cited alongside, same era.
W. Sun, G. Tang, and K. Hauser, “Fast uav trajectory optimization using bilevel optimization with analytical gradients,” in 2020 American Control Conference (ACC) , 2020, pp. 82–87
2020
Cited alongside, same era.
D. Burke, A. Chapman, and I. Shames, “Generating minimum-snap quadrotor trajectories really fast,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 1487–1492
2020
Cited alongside, same era.
S. Bansal, V. Tolani, S. Gupta, J. Malik, and C. Tomlin, “Combining optimal control and learning for visual navigation in novel environments,” in Proceedings of the Conference on Robot Learning , ser. Proceedings of Machine Learning Research, L. P. Kaelbling, D. Kragic, and K. Sugiura, Eds., vol. 100. PMLR, 30 Oct–01 Nov 2020, pp. 420–429. [Online]. Available: https://proceedings.mlr.press/v100/bansal20a.html
2020
Cited alongside, same era.
T. Frerix, M. Nießner, and D. Cremers, “Homogeneous linear inequality constraints for neural network activations,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2020, pp. 3229–3234
2020
Cited alongside, same era.
R. Penicka, Y. Song, E. Kaufmann, and D. Scaramuzza, “Learning minimum-time flight in cluttered environments,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 7209–7216, 2022
2022
Later among the works it cites.
Z. Wang, X. Zhou, C. Xu, and F. Gao, “Geometrically constrained trajectory optimization for multicopters,” IEEE Transactions on Robotics , vol. 38, no. 5, pp. 3259–3278, 2022
2022
Later among the works it cites.
R. Penicka and D. Scaramuzza, “Minimum-time quadrotor waypoint flight in cluttered environments,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 5719–5726, 2022
2022
Later among the works it cites.
N. Jaquier, Y. Zhou, J. Starke, and T. Asfour, “Learning to sequence and blend robot skills via differentiable optimization,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 8431–8438, 2022
2022
Later among the works it cites.
Y. Tao, Y. Wu, B. Li, F. Cladera, A. Zhou, D. Thakur, and V. Kumar, “Seer: Safe efficient exploration for aerial robots using learning to predict information gain,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 1235–1241
2023
Closest in time.
2023
Closest in time.
G. Ryou, E. Tal, and S. Karaman, “Real-time generation of time-optimal quadrotor trajectories with semi-supervised seq2seq learning,” in Proceedings of The 6th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, K. Liu, D. Kulic, and J. Ichnowski, Eds., vol. 205. PMLR, 14–18 Dec 2023, pp. 1860–1870. [Online]. Available: https://proceedings.mlr.press/v205/ryou23a.html
2023
Closest in time.
G. Négiar, M. W. Mahoney, and A. Krishnapriyan, “Learning differentiable solvers for systems with hard constraints,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=vdv6CmGksr0
2023
Closest in time.
K. Chaney, F. Cladera, Z. Wang, A. Bisulco, M. A. Hsieh, C. Korpela, V. Kumar, C. J. Taylor, and K. Daniilidis, “M3ed: Multi-robot, multi-sensor, multi-environment event dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2023, pp. 4015–4022
2023
Closest in time.