This article focuses on distributionally robust controller design for safe navigation in the presence of dynamic and stochastic obstacles, where the true probability distributions associated with the disturbances are unknown. Although the true probability distributions are considered to be unknown, they are considered to belong to a set of probability distributions known as the ambiguity set. This ambiguity set includes all the probability distributions that share the same first two moments. In this article, the safe navigation problem has been defined by an optimal control problem with probabilistic collision avoidance constraint. To ensure satisfaction of this probabilistic constraint in the presence of disturbances whose true probability distributions are known, this constraint has been enforced in a distributionally robust sense. A computationally tractable control approach has been presented in this article that exploits techniques from robust optimization methods. Simulation results show the effectiveness of the proposed method.
HossainMMPengC.Predictive event-triggered load frequency control for hybrid power systems under denial-of-service attacks. IET Generat Trans Distrib2020; 14(26): 6457–6467.
2.
KatriniokARosariusBMähönenP.Fully distributed model predictive control of connected automated vehicles in intersections: theory and vehicle experiments. IEEE Trans Intell Transp Syst. Epub ahead of print 31March2022. DOI: 10.1109/TITS.2022.3162038.
MesbahA.Stochastic model predictive control: an overview and perspectives for future research. IEEE Contr Syst Mag2016; 36(6): 30–44.
5.
PaulsonJABuehlerEABraatzRD, et al. Stochastic model predictive control with joint chance constraints. Int J Contr2020; 93(1): 126–139.
6.
ChiangHTLHomChaudhuriBVinodAP, et al. Dynamic risk tolerance: motion planning by balancing short-term long-term stochastic dynamic predictions In: International conference on robotics and automation, 2017, pp.3762–3769, https://www.cs.unm.edu/tapialab/Publications/50.pdf
7.
FioriniPShillerZ.Motion planning in dynamic environments using velocity obstacles. Int J Robot Res1998; 17(7): 760–772.
LaValleSM.Planning algorithms. Cambridge: Cambridge University Press, 2006.
10.
SchouwenaarsTDe MoorBFeronE, et al. Mixed integer programming for multi-vehicle path planning. In: European control conference, Porto, 4–7September2001, pp.2603–2608. New York: IEEE.
11.
CrowleyJZelekeYAltmB, et al. Set-based predictive control for collision detection and evasion. In: IEEE international conference on automation science and engineering, Vancouver, BC, Canada, 22–26August2019, pp.541–546. New York: IEEE.
12.
PereiraAAlthoffM.Overapproximative human arm occupancy prediction for collision avoidance. IEEE Trans Automat Sci Eng2017; 15(2): 818–831.
13.
WeiYWeiJZouJ.Collision avoidance in an agent system for crowd simulation. In: 13th IEEE conference on automation science and engineering (CASE), Xi’an, China, 20–23August2017, pp.1380–1385. New York: IEEE.
FuadMAgustinahTPurwantoD.Collision avoidance of multi modal moving objects for mobile robot using hybrid velocity obstacles. Int J Intell Eng Syst2020; 13(3): 407–421.
16.
SongJHaoCSuJ.Path planning for unmanned surface vehicle based on predictive artificial potential field. Int J Adv Robot Syst2020; 17(2): 1729881420918461.
17.
QiJYangHSunH.Mod-rrt*: a sampling-based algorithm for robot path planning in dynamic environment. IEEE Trans Indus Electron2020; 68(8): 7244–7251.
18.
WangXLuoXHanB, et al. Collision-free path planning method for robots based on an improved rapidly-exploring random tree algorithm. Appl Sci2020; 10(4): 1381.
19.
ChowYLPavoneM.A framework for time-consistent, risk-averse model predictive control: theory and algorithms. In: American control conference (ACC), Portland, OR, 4–6June2014, pp.4204–4211. New York: IEEE.
20.
HercegDSopasakisPBemporadA, et al. Risk-averse model predictive control. Automatica2019; 100: 281–288.
21.
Van ParysBPKuhnDGoulartPJ, et al. Distributionally robust control of constrained stochastic systems. IEEE Trans Automat Contr2016; 61(2): 430–442.
22.
ShapiroADentchevaDRuszczyńskiA.Lectures on stochastic programming: modeling and theory. Philadelphia, PA: SIAM, 2009.
23.
YangWXuH.Distributionally robust chance constraints for non-linear uncertainties. Math Program2016; 155(1–2): 231–265.
24.
ZymlerSKuhnDRustemB.Distributionally robust joint chance constraints with second-order moment information. Math Programm2013; 137(1–2): 167–198.
25.
HakobyanAKimGCYangI.Risk-aware motion planning and control using CVAR-constrained optimization. IEEE Robot Automat Lett2019; 4(4): 3924–3931.
26.
HakobyanAYangI.Wasserstein distributionally robust motion control for collision avoidance using conditional value-at-risk, 2001, https://arxiv.org/abs/2001.04727
27.
SummersT.Distributionally robust sampling-based motion planning under uncertainty. In: IEEE international conference on intelligent robots and systems, Madrid, 1–5October2018, pp.6518–6523. New York: IEEE.
28.
HomChaudhuriB.Distributionally robust stochastic model predictive control for collision avoidance. ASME Dynam Syst Contr Conf2019; 59155: V002T14A0052019.
29.
MaoYDueriDSzmukM, et al. Successive convexification of non-convex optimal control problems with state constraints. Ifac-PapersOnline2017; 50(1): 4063–4069.
30.
WangYBoydS.Fast model predictive control using online optimization. IEEE Trans Contr Syst Technol2009; 18(2): 267–278.
31.
Ocampo-MartinezCPuigVCembranoG, et al. Application of predictive control strategies to the management of complex networks in the urban water cycle [applications of control]. IEEE Contr Syst Mag2013; 33(1): 15–41.
32.
WangYOcampo-MartinezCPuigV.Stochastic model predictive control based on Gaussian processes applied to drinking water networks. IET Contr Theory Appl2016; 10(8): 947–955.
