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In this paper, we address the risk estimation problem where one aims at estimating the probability of violation of safety constraints for a robot in the presence of bounded uncertainties with arbitrary probability distributions.
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P. Florence, J. Carter, R. Tedrake, ”Integrated perception and control at high speed : Evaluating collision avoidance maneuvers without Maps”, 12th International workshop on the algorithm foundations of robotics (WAFR), San Francisco, 2016
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P. Santana, T. Vaquero, C. Toledo, A. Wang, C. Fang, B. Williams ”PARIS: a polynomial-time, risk-sensitive scheduling algorithm for probabilistic simple temporal networks with uncertainty”, 26th International Conference on Automated Planning and Scheduling (ICAPS), London, 2016
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E. Schmerling, M. Pavone, ”Evaluating trajectory collision probability through adaptive importance sampling for safe motion planning” Robotics: Science and Systems (RSS), MIT, 2017
2017
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M. Ono, B. Williams, L. Blackmore, ”Probabilistic planning for continuous dynamic systems under bounded risk”, Journal of Artificial Intelligence Research, vol. 46, 511–577, 2013
2013
Cited alongside, same era.
F. Dabbene, D. Henrion, ”Set approximation via minimum-volume polynomial sublevel sets”, European Control Conference (ECC), Switzerland, 2013
2013
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C. Fang, P. Yu, B. C. Williams, ”Chance-constrained probabilistic simple temporal problems”, 28th AAAI Conference on Artificial Intelligence (AAAI), Québec, 2014
2014
Cited alongside, same era.
W. Liu, M. H. Ang, ”Incremental sampling-based algorithm for risk-aware planning under motion uncertainty”, IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 2014
2014
Cited alongside, same era.
Later among the works it cites.
A. A. Ahmadi, G. Hall, A. Makadia, V. Sindhwani, ”Geometry of 3D environments and sum of squares polynomials”, Robotics: Science and Systems (RSS), MIT, 2017
2017
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D. Papp, ”Semi-infinite programming using high-degree polynomial interpolants and semidefinite programming”, SIAM Journal on Optimization, 27(3), 1858-–1879, 2017
2017
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L. Janson, E. Schmerling, M. Pavone, ”Monte Carlo motion planning for robot trajectory optimization under uncertainty”, Springer Proceedings in Advance Robotics Book Series 3, Robotics Research, vol. 2, pp. 343–361, 2018
2018
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