25 citations · 67 across the 17 of their papers we have counts for
21 papers
RoboAtlas: Contextual Active SLAM
Alexander Schperberg, Shivam K. Panda, Abraham P. Vinod +4
We present RoboAtlas, a contextual Active SLAM framework that adaptively balances geometric exploration and semantic reasoning using a scalable 3D semantic mapping system, OpenRobo…
Autonomous Navigation and Station-Keeping on Near-Rectilinear Halo Orbits
Yuri Shimane, Karl Berntorp, Stefano Di Cairano +1
This article develops an optical navigation (OPNAV) and station-keeping pipeline for the near-rectilinear halo orbit (NRHO) in high-fidelity ephemeris model dynamics, using synthet…
MOBIUS: A Multi-Modal Bipedal Robot that can Walk, Crawl, Climb, and Roll
Alexander Schperberg, Yusuke Tanaka, Stefano Di Cairano +1
This paper presents the MOBIUS platform, a bipedal robot capable of walking, crawling, climbing, and rolling. MOBIUS features four limbs, two 6-DoF arms with two-finger grippers fo…
Data-Driven Motion Planning for Uncertain Nonlinear Systems
Babak Esmaeili, Hamidreza Modares, Stefano Di Cairano
This paper proposes a data-driven motion-planning framework for nonlinear systems that constructs a sequence of overlapping invariant polytopes. Around each randomly sampled waypoi…
Revolution-Spaced Output-Feedback Model Predictive Control for Station Keeping on Near-Rectilinear Halo Orbits
Yuri Shimane, Stefano Di Cairano, Koki Ho +1
We develop a model predictive control (MPC) policy for station keeping on a Near-Rectilinear Halo Orbit (NRHO). The proposed policy achieves full-state tracking of a reference NRHO…
Path Planning and Motion Control for Accurate Positioning of Car-like Robots
Jin Dai, Zejiang Wang, Yebin Wang +2
This paper investigates the planning and control for accurate positioning of car-like robots. We propose a solution that integrates two modules: a motion planner, facilitated by th…
Equivariant Deep Learning of Mixed-Integer Optimal Control Solutions for Vehicle Decision Making and Motion Planning
Rudolf Reiter, Rien Quirynen, Moritz Diehl +1
Mixed-integer quadratic programs (MIQPs) are a versatile way of formulating vehicle decision making and motion planning problems, where the prediction model is a hybrid dynamical s…
Projection-free computation of robust controllable sets with constrained zonotopes
Abraham P. Vinod, Avishai Weiss, Stefano Di Cairano
We study the problem of computing robust controllable sets for discrete-time linear systems with additive uncertainty. We propose a tractable and scalable approach to inner- and ou…
Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning
Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty +3
We consider the problem of safe multi-agent motion planning for drones in uncertain, cluttered workspaces. For this problem, we present a tractable motion planner that builds upon…
Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning
Rien Quirynen, Sleiman Safaoui, Stefano Di Cairano
We develop a real-time feasible mixed-integer programming-based decision making (MIP-DM) system for automated driving. Using a linear vehicle model in a road-aligned coordinate fra…
Robust Parameter Estimation for Hybrid Dynamical Systems
Ryan S. Johnson, Stefano Di Cairano, Ricardo G. Sanfelice
We consider the problem of estimating a vector of unknown constant parameters for a class of hybrid dynamical systems -- that is, systems whose state variables exhibit both continu…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Tailored Presolve Techniques in Branch-and-Bound Method for Fast Mixed-Integer Optimal Control Applications
Rien Quirynen, Stefano Di Cairano
Mixed-integer model predictive control (MI-MPC) can be a powerful tool for modeling hybrid control systems. In case of a linear-quadratic objective in combination with linear or pi…
Automated Controller Calibration by Kalman Filtering
Marcel Menner, Karl Berntorp, Stefano Di Cairano
This paper proposes a method for calibrating control parameters. Examples of such control parameters are gains of PID controllers, weights of a cost function for optimal control, f…
A Structure Exploiting Branch-and-Bound Algorithm for Mixed-Integer Model Predictive Control
Pedro Hespanhol, Rien Quirynen, Stefano Di Cairano
Mixed-integer model predictive control (MI-MPC) requires the solution of a mixed-integer quadratic program (MIQP) at each sampling instant under strict timing constraints, where pa…
Path Planning using Positive Invariant Sets
Claus Danielson, Avishai Weiss, Karl Berntorp +1
We present an algorithm for steering the output of a linear system from a feasible initial condition to a desired target position, while satisfying input constraints and non-convex…
Indirect-adaptive Model Predictive Control for Linear Systems with Polytopic Uncertainty
Stefano Di Cairano
We develop an indirect-adaptive model predictive control algorithm for uncertain linear systems subject to constraints. The system is modeled as a polytopic linear parameter varyin…
Explicit model predictive control accuracy analysis
Andrew Knyazev, Peizhen Zhu, Stefano Di Cairano
Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be…
MPC on manifolds with an application to the control of spacecraft attitude on SO(3)
Uroš Kalabić, Rohit Gupta, Stefano Di Cairano +2
We develop a model predictive control (MPC) design for systems with discrete-time dynamics evolving on smooth manifolds. We show that the properties of conventional MPC for dynamic…
ADMM for Convex Quadratic Programs: Q-Linear Convergence and Infeasibility Detection
Arvind U. Raghunathan, Stefano Di Cairano
In this paper, we analyze the convergence of Alternating Direction Method of Multipliers (ADMM) on convex quadratic programs (QPs) with linear equality and bound constraints. The A…
Extremum Seeking-based Iterative Learning Linear MPC
Mouhacine Benosman, Stefano Di Cairano, Avishai Weiss
In this work we study the problem of adaptive MPC for linear time-invariant uncertain models. We assume linear models with parametric uncertainties, and propose an iterative multi-…