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Modeling the precise dynamics of off-road vehicles is a complex yet essential task due to the challenging terrain they encounter and the need for optimal performance and safety.
G. E. Uhlenbeck and L. S. Ornstein, “On the theory of the brownian motion,” Physical review , vol. 36, no. 5, p. 823, 1930
1930
Earlier work this paper cites.
H. B. Pacejka and E. Bakker, “The magic formula tyre model,” Vehicle System Dynamics , vol. 21, pp. 1–18, 1991. [Online]. Available: https://api.semanticscholar.org/CorpusID:108456393
1991
Earlier work this paper cites.
E. Krotkov and J. Blitch, “The defense advanced research projects agency (darpa) tactical mobile robotics program,” The International Journal of Robotics Research , vol. 18, no. 7, pp. 769–776, 1999
1999
Earlier work this paper cites.
T. Chang, T. Hong, M. N. Abrams, and M. Shneier, “An intelligent world model for autonomous off-road driving,” Computer Vision and Image Understanding , 2001. [Online]. Available: https://api.semanticscholar.org/CorpusID:118762171
2001
Earlier work this paper cites.
E. Süli and D. F. Mayers, “An introduction to numerical analysis,” 2003. [Online]. Available: https://api.semanticscholar.org/CorpusID:118602067
2003
Earlier work this paper cites.
S. Thrun, “Stanley: The robot that won the darpa grand challenge,” Journal of Field Robotics , vol. 23, 2006. [Online]. Available: https://api.semanticscholar.org/CorpusID:1438204
2006
Earlier work this paper cites.
K. Chu, M. Lee, and M. Sunwoo, “Local path planning for off-road autonomous driving with avoidance of static obstacles,” IEEE Transactions on Intelligent Transportation Systems , vol. 13, no. 4, pp. 1599–1616, 2012
2012
Earlier work this paper cites.
H. Mousazadeh, “A technical review on navigation systems of agricultural autonomous off-road vehicles,” Journal of Terramechanics , vol. 50, no. 3, pp. 211–232, 2013. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0022489813000220
2013
Earlier work this paper cites.
J. E. Naranjo, M. Clavijo, F. Jiménez et al. , “Autonomous vehicle for surveillance missions in off-road environment,” in 2016 IEEE Intelligent Vehicles Symposium (IV) , 2016, pp. 98–103
2016
Earlier work this paper cites.
N. Seegmiller and A. Kelly, “High-fidelity yet fast dynamic models of wheeled mobile robots,” IEEE Transactions on Robotics , vol. 32, no. 3, pp. 614–625, 2016
2016
Earlier work this paper cites.
K. Berns, A. Nezhadfard, M. Tosa et al. , Unmanned Ground Robots for Rescue Tasks , 08 2017
2017
Earlier work this paper cites.
A. Bezzina, L. Xuereb, S. G. Fabri, and C. J. Debono, “Development of an autonomous off-road vehicle for military applications,” Robotics and Autonomous Systems , vol. 94, pp. 52–61, 2017
2017
Earlier work this paper cites.
G. Williams, N. Wagener, B. Goldfain et al. , “Information theoretic mpc for model-based reinforcement learning,” in 2017 IEEE International Conference on Robotics and Automation . IEEE, 2017, pp. 1714–1721
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
S.-Y. Jeon, R. Chung, and D. Lee, “Tire force estimation of dynamic wheeled mobile robots using tire-model based constrained kalman filtering,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 2470–2477
2018
Cited alongside, same era.
Y. Zhou, C. Barnes, J. Lu et al. , “On the continuity of rotation representations in neural networks,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 5738–5746, 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:56178817
2018
Cited alongside, same era.
M. Raissi, P. Perdikaris, and G. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” Journal of Computational Physics , vol. 378, pp. 686–707, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0021999118307125
2019
Cited alongside, same era.
2022
Later among the works it cites.
A. Ghobadpour, G. Monsalve, A. Cardenas, and H. Mousazadeh, “Off-road electric vehicles and autonomous robots in agricultural sector: Trends, challenges, and opportunities,” Vehicles , vol. 4, no. 3, pp. 843–864, 2022. [Online]. Available: https://www.mdpi.com/2624-8921/4/3/47
2022
Later among the works it cites.
A. Saviolo, G. Li, and G. Loianno, “Physics-inspired temporal learning of quadrotor dynamics for accurate model predictive trajectory tracking,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 10 256–10 263, oct 2022. [Online]. Available: https://doi.org/10.1109%2Flra.2022.3192609
2022
Later among the works it cites.
S. Sanyal and K. Roy, “Ramp-net: A robust adaptive mpc for quadrotors via physics-informed neural network,” 2023 IEEE International Conference on Robotics and Automation (ICRA) , pp. 1019–1025, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:252367666
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Z. Han, S. Yuan, X. Li, and J. Zhou, “Enhanced closed-loop systematic kinematics analysis of wheeled mobile robots,” International Journal of Advanced Robotic Systems , vol. 16, no. 4, p. 1729881419863242, 2019. [Online]. Available: https://doi.org/10.1177/1729881419863242
2019
Cited alongside, same era.
S. Kakkar and M. A. Minor, “Fast and reliable motion model for articulated wheeled mobile robots on extremely rough and rocky terrains,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 2252–2259, 2019
2019
Cited alongside, same era.
F. Rubio, C. Llopis-Albert, F. Valero, and A. J. Besa, “A new approach to the kinematic modeling of a three-dimensional car-like robot with differential drive using computational mechanics,” Advances in Mechanical Engineering , vol. 11, no. 3, p. 1687814019825907, 2019. [Online]. Available: https://doi.org/10.1177/1687814019825907
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 . Curran Associates, Inc., 2019, pp. 8024–8035. [Online]. Available: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
2019
Cited alongside, same era.
J. Tremblay, T. Manderson, A. Noca et al. , “Multimodal dynamics modeling for off-road autonomous vehicles,” 2021 IEEE International Conference on Robotics and Automation (ICRA) , pp. 1796–1802, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:227151313
2020
Cited alongside, same era.
J. Tremblay, T. Manderson, A. Noca et al. , “Multimodal dynamics modeling for off-road autonomous vehicles,” 2021 IEEE International Conference on Robotics and Automation (ICRA) , pp. 1796–1802, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:227151313
2020
Cited alongside, same era.
J. Mai, “System design, modelling, and control for an off-road autonomous ground vehicle,” Master’s thesis, Carnegie Mellon University, Pittsburgh, PA, July 2020
2020
Cited alongside, same era.
W. Wang, Y. Hu, and S. Scherer, “Tartanvo: A generalizable learning-based vo,” Conference on Robot Learning , 2020
2020
Cited alongside, same era.
K. Y. Chee, T. Z. Jiahao, and M. A. Hsieh, “Knode-mpc: A knowledge-based data-driven predictive control framework for aerial robots,” IEEE Robotics and Automation Letters , vol. 7, pp. 2819–2826, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:237485443
2021
Cited alongside, same era.
2022
Later among the works it cites.
W. Sun, N. Akashi, Y. Kuniyoshi, and K. Nakajima, “Physics-informed recurrent neural networks for soft pneumatic actuators,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 6862–6869, 2022
2022
Later among the works it cites.
W. Luo, Z. Yan, Q. Song, and R. Tan, “Physics-directed data augmentation for deep model transfer to specific sensor,” ACM Trans. Sen. Netw. , vol. 19, no. 1, dec 2022. [Online]. Available: https://doi.org/10.1145/3549076
2022
Later among the works it cites.
S. J. Wang, S. Triest, W. Wang et al. , “Rough terrain navigation using divergence constrained model-based reinforcement learning,” in Proceedings of the 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 08–11 Nov 2022, pp. 224–233. [Online]. Available: https://proceedings.mlr.press/v164/wang22c.html
2022
Later among the works it cites.
T. Kim, H. Lee, and W. Lee, “Physics embedded neural network vehicle model and applications in risk-aware autonomous driving using latent features,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 4182–4189
2022
Later among the works it cites.
X. Meng, N. Hatch, A. Lambert et al. , “Terrainnet: Visual modeling of complex terrain for high-speed, off-road navigation,” 2023
2023
Closest in time.
M. G. Castro, S. Triest, W. Wang et al. , “How does it feel? self-supervised costmap learning for off-road vehicle traversability,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 931–938
2023
Closest in time.
A. Gadekar, S. Fulsundar, P. Deshmukh et al. , “Rakshak: A modular unmanned ground vehicle for surveillance and logistics operations,” Cognitive Robotics , vol. 3, pp. 23–33, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2667241323000083
2023
Closest in time.
2023
Closest in time.
S. Triest, M. G. Castro, P. Maheshwari et al. , “Learning risk-aware costmaps via inverse reinforcement learning for off-road navigation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 924–930
2023
Closest in time.