Fetching the paper…
Reading the bibliography…
In this paper, a risk-aware motion control scheme is considered for mobile robots to avoid randomly moving obstacles when the true probability distribution of uncertainty is unknown.
G. P. McCormick, “Computability of global solutions to factorable nonconvex programs: Part I—Convex underestimating problems,” Mathematical Programming , vol. 10, no. 1, pp. 147–175, 1976
1976
Earlier work this paper cites.
E. M. B. Smith, “On the optimal design of continuous processes.” 1997
1997
Earlier work this paper cites.
P. Artzner, F. Delbaen, J.-M. Eber, and D. Heath, “Coherent measures of risk,” Mathematical Finance , vol. 9, no. 3, pp. 203–228, 1999
1999
Earlier work this paper cites.
R. T. Rockafellar and S. Uryasev, “Conditional value-at-risk for general loss distribution,” Journal of Banking & Finance , vol. 26, pp. 1443–1471, 2002
2002
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex Optimization . Cambridge University Press, 2004
2004
Earlier work this paper cites.
G. C. Calafiore and L. El Ghaoui, “On distributionally robust chance-constrained linear programs,” Journal of Optimization Theory and Applications , vol. 130, no. 1, pp. 1–22, 2006
2006
Earlier work this paper cites.
J. Nocedal and S. Wright, Numerical Optimization . Springer Science & Business Media, 2006
2006
Earlier work this paper cites.
L. Liberti, “Introduction to global optimization,” Ecole Polytechnique , 2008
2008
Earlier work this paper cites.
B. Luders, M. Kothari, and J. How, “Chance constrained RRT for probabilistic robustness to environmental uncertainty,” in AIAA Guidance, Navigation, and Control Conference , 2010
2010
Earlier work this paper cites.
L. Blackmore, M. Ono, A. Bektassov, and B. C. Williams, “A probabilistic particle-control approximation of chance-constrained stochastic predictive control,” IEEE Transactions on Robotics , vol. 26, no. 3, pp. 502–517, 2010
2010
Earlier work this paper cites.
E. Delage and Y. Ye, “Distributionally robust optimization under moment uncertainty with application to data-driven problems,” Operations Research , vol. 58, no. 3, pp. 595–612, 2010
2010
Earlier work this paper cites.
——, “Optimal kinodynamic motion planning using incremental sampling-based methods,” in IEEE Conference on Decision and Control , 2010
2010
Earlier work this paper cites.
A. Bry and N. Roy, “Rapidly-exploring random belief trees for motion planning under uncertainty,” in IEEE International Conference on Robotics and Automation , 2011
2011
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
Cited alongside, same era.
S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” The International Journal of Robotics Research , vol. 30, no. 7, pp. 846–894, 2011
2011
Cited alongside, same era.
R. Rajamani, Vehicle Dynamics and Control . Springer Science & Business Media, 2011
2011
Cited alongside, same era.
N. E. Du Toit and J. W. Burdick, “Robot motion planning in dynamic, uncertain environments,” IEEE Transactions on Robotics , vol. 28, no. 1, pp. 101–115, 2012
2012
Cited alongside, same era.
H. Xu and S. Mannor, “Distributionally robust Markov decision processes,” Mathematics of Operations Research , vol. 37, no. 2, pp. 288–300, 2012
A. Majumdar and M. Pavone, “How should a robot assess risk? towards an axiomatic theory of risk in robotics,” in International Symposium on Robotics Research , 2017
2017
Later among the works it cites.
I. Yang, “A convex optimization approach to distributionally robust Markov decision processes with Wasserstein distance,” IEEE Control Systems Letters , vol. 1, no. 1, pp. 164–169, 2017
2017
Later among the works it cites.
T. Summers, “Distributionally robust sampling-based motion planning under uncertainty,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2018
2018
Later among the works it cites.
P. Mohajerin Esfahani and D. Kuhn, “Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations,” Mathematical Programming , vol. 171, pp. 115–166, 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2012
Cited alongside, same era.
A. Ben-Tal, D. Den Hertog, A. De Waegenaere, B. Melenberg, and G. Rennen, “Robust solutions of optimization problems affected by uncertain probabilities,” Management Science , vol. 59, no. 2, pp. 341–357, 2013
2013
Cited alongside, same era.
A. Shapiro, D. Dentcheva, and A. Ruszczyński, Lectures on Stochastic Programming: Modeling and Theory , 2nd ed. SIAM, 2014
2014
Cited alongside, same era.
W. Wiesemann, D. Kuhn, and M. Sim, “Distributionally robust convex optimization,” Operations Research , vol. 62, no. 6, pp. 1358–1376, 2014
2014
Cited alongside, same era.
A. Jasour, N. S. Aybat, and C. M. Lagoa, “Semidefinite programming for chance constrained optimization over semialgebraic sets,” SIAM Journal on Optimization , vol. 25, no. 3, pp. 1411–1440, 2015
2015
Cited alongside, same era.
Y. Chow, A. Tamar, S. Mannor, and M. Pavone, “Risk-sensitive and robust decision-making: a CVaR optimization approach,” in Advances in Neural Information Processing Systems , 2015
2015
Cited alongside, same era.
G. Bayraksan and D. K. Love, “Data-driven stochastic programming using phi-divergences,” Tutorials in Operations Research , pp. 1–19, 2015
2015
Cited alongside, same era.
N. Fournier and A. Guillin, “On the rate of convergence in Wasserstein distance of the empirical measure,” Probability Theory and Related Fields , vol. 162, no. 3–4, pp. 707–738, 2015
2015
Cited alongside, same era.
C. Zhao and Y. Guan, “Data-driven risk-averse stochastic optimization with Wasserstein metric,” Operations Research Letters , vol. 46, no. 2, 2018
2018
Later among the works it cites.
S. Samuelson and I. Yang, “Safety-aware optimal control of stochastic systems using conditional value-at-risk,” in American Control Conference , 2018
2018
Later among the works it cites.
——, “A dynamic game approach to distributionally robust safety specifications for stochastic systems,” Automatica , vol. 94, pp. 94–101, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Singh, Y.-L. Chow, A. Majumdar, and M. Pavone, “A framework for time-consistent, risk-sensitive model predictive control: Theory and algorithms,” IEEE Transactions on Automatic Control , vol. 64, no. 7, pp. 2905–2912, 2019
2019
Later among the works it cites.
A. Hakobyan, G. C. Kim, and I. Yang, “Risk-aware motion planning and control using CVaR-constrained optimization,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 3924–3931, 2019
2019
Later among the works it cites.
A. R. Hota, A. Cherukuri, and J. Lygeros, “Data-driven chance constrained optimization under Wasserstein ambiguity sets,” in American Control Conference , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
I. Tzortzis, C. D. Charalambous, and T. Charalambous, “Infinite horizon average cost dynamic programming subject to total variation distance ambiguity,” SIAM Journal on Control and Optimization , vol. 57, no. 4, pp. 2843–2872, 2019
2019
Later among the works it cites.