arXiv subjects

Dimitris Boskos

Publications and source records attributed to Dimitris Boskos.

At least 19 recordsLinked to original sources

arXiv2026

Entanglement-Free Trajectory Planning for Tethered Mobile Robots with a Slack Tether

In motion planning algorithms for tethered mobile robots, the entanglement state of the tether is a critical aspect to consider during the planning phase. This is particularly important in case of a slack tether, where the shape of the tether is not determined solely by the geometry of the environment and the location of the obstacles, but also by the dynamics of the tether, by the trajectory followed by the robot, and possibly by exogenous forces. In this scenario, preventing entanglement requires planning a robot trajectory that accounts for the entanglement definition and for the dynamics of the robot and of the tether. In this work, we propose a motion planning algorithm for tethered mobile robots with a slack tether that computes dynamically feasible entanglement-free trajectories to navigate through an environment with static obstacles. By considering the entanglement state during all the stages of the planning pipeline, we are able to compute safer trajectories that avoid entanglement during the motion of the robot. We achieve this through a three-step pipeline, which includes (i) the construction of a topological model of the entanglement-free configuration space of the tethered robot, (ii) the generation of a set of candidate paths using this model, and (iii) the computation of a dynamically feasible entanglement-free trajectory by solving a homotopy-constrained trajectory generation problem. The resulting trajectory can then be executed to lead the robot to its target location, while maintaining the tether in an entanglement-free configuration. We demonstrate the benefits of this algorithm in simulations, where we show how the planning algorithm avoids violations of the entanglement constraints, resulting in safer and more reliable trajectories.

cs.RO↗

arXiv2026

Robust Operation of Distribution Networks: Generalized Uncertainty Modelling in Confidence-Level-Based Information Gap Decision

This paper studies the robust optimal operation of distribution networks (DNs) under renewable generation and load demand uncertainties, seeking an improved trade-off between robustness and economic performance. Building upon information gap decision theory (IGDT), a generalized uncertainty modelling is proposed to enhance the expressiveness of the uncertainty characterization. The proposed modelling captures both symmetric and asymmetric uncertainty features, and supports linear or nonlinear expansion of the uncertainty sets driven by confidence level. This advancement leads to the development of a confidence-level-based IGDT (CL-IGDT) framework for DN operation. To solve the resulting model, its equivalence to a family of two-stage robust optimization problems (TSROs) is established, enabling a Fibonacci search over the confidence level. To further improve computational efficiency, a cut-recycling strategy is proposed to exploit invariant information across TSROs. These techniques are integrated into a novel Fibonacci-Parametric Column-and-Constraint Generation algorithm with guaranteed asymptotic convergence. Case studies validate the effectiveness of the proposed framework and demonstrate the performance advantages of the proposed algorithm.

eess.SY↗

arXiv2026

Parametric Reduced-Order modeling and Closed-Loop Control of Tandem-Cylinder Wakes

The flow around two circular cylinders arranged in a tandem exhibits complex wake interactions that lead to amplified unsteady loads, particularly in the co-shedding regime where a fully developed wake forms in the gap between the cylinders. Although various control strategies have been proposed to mitigate these effects, most prior studies have focused primarily on load alleviation. Complete suppression of vortex shedding, both in the gap region and in the wake of the second cylinder, has so far only been achieved using open-loop approaches. In this work, we propose a closed-loop control framework for suppressing vortex shedding in tandem cylinder flows in the co-shedding regime. Focusing on low Reynolds numbers and sufficiently large spacings, we derive a parametric reduced-order model using a global weakly nonlinear analysis of the incompressible Navier-Stokes equations. The model is generalized to account for time dependent forcing and facilitates the real time prediction of the flow evolution. Using this model, we design a model predictive controller and apply it to the full-order system via velocity measurements and volumetric forcing. The approach is demonstrated for a cylinder spacing of eight diameters. Vortex shedding is fully suppressed in both the gap region and the downstream wake for Reynolds numbers $Re=50$, $60$, and $70$, while a significant reduction in flow unsteadiness is achieved at $Re=80$. We further show that effective control is possible with limited sensing: suppression is achieved using a single measurement point for $Re=50$ and two-point measurements for $Re=60$ and $70$.

physics.flu-dyn↗

arXiv2026

Transonic Buffet Modeling via Invariant Manifolds

In transonic flow over aircraft wings, shock-boundary-layer interactions can give rise to transonic buffet, which degrades maneuverability through unsteady aerodynamic loads. Beyond its practical importance, two-dimensional transonic buffet represents a canonical example of a global instability for which reduced-order modeling remains challenging due to nonlinearity, sharp spatial gradients, and the coexistence of an unstable equilibrium with an attracting limit cycle. Commonly, reduced-order models of such phenomena capture nonlinear dynamics only in aerodynamic observables, while prediction of the full flow state is achieved through linear representations valid only near the unstable equilibrium or on the limit cycle. In this work, we present a reduced-order model that predicts the nonlinear evolution of the full flow field by exploiting the existence of an attracting two-dimensional invariant manifold. We adapt an existing data-driven framework for identifying invariant manifolds and the associated reduced dynamics, making it suitable for scaling to large-scale CFD applications. The invariant manifold is identified as a graph over its tangent space using an iterative encoder-update and the reduced dynamics are obtained via least-squares regression. A subsequent extended normal-form transformation enables physical interpretability of the model through a modal decomposition of the flow. The reduced-order model is identified for transonic buffet over the OAT15A supercritical airfoil, showing that it is possible to achieve this accurately using just a single training trajectory. Validation against independent simulations demonstrates accurate prediction of nonlinear behavior, together with reliable reconstruction of the full flow field, particularly in the late-transient and limit-cycle regimes.

physics.flu-dyn↗

arXiv2026

Reinforcement Learning with Distributed MPC for Fuel-Efficient Platoon Control with Discrete Gear Transitions

Cooperative control of groups of autonomous vehicles (AVs), i.e., platoons, is a promising direction to improving the efficiency of autonomous transportation systems. In this context, distributed co-optimization of both vehicle speed and gear position can offer benefits for fuel-efficient driving. To this end, model predictive control (MPC) is a popular approach, optimizing the speed and gear-shift schedule while explicitly considering the vehicles' dynamics over a prediction window. However, optimization over both the vehicles' continuous dynamics and discrete gear positions is computationally intensive, and may require overly long sample times or high-end hardware for real-time implementation. This work proposes a reinforcement learning (RL)-based distributed MPC approach to address this issue. For each vehicle in the platoon, a policy is trained to select and fix the gear positions across the prediction window of a local MPC controller, leaving a significantly simpler continuous optimization problem to be solved as part of a distributed MPC scheme. In order to reduce the computational cost of training and facilitate the scalability of the proposed approach to large platoons, the policies are parameterized such that the emergent multi-agent RL problem can be decoupled into single-agent learning tasks. In addition, a recurrent neural-network (RNN) architecture is proposed for the gear selection policy, such that the learning is scalable even as the number of possible gear-shift schedules grows exponentially with the MPC prediction horizon. In highway-driving simulations, the proposed approach is shown to have a significantly lower computation burden and a comparable performance in terms of fuel-efficient platoon control, with respect to pure MPC-based co-optimization.

eess.SY↗

arXiv2025

Efficient Computation of a Continuous Topological Model of the Configuration Space of Tethered Mobile Robots

Despite the attention that the problem of path planning for tethered robots has garnered in the past few decades, the approaches proposed to solve it typically rely on a discrete representation of the configuration space and do not exploit a model that can simultaneously capture the topological information of the tether and the continuous location of the robot. In this work, we explicitly build a topological model of the configuration space of a tethered robot starting from a polygonal representation of the workspace where the robot moves. To do so, we first establish a link between the configuration space of the tethered robot and the universal covering space of the workspace, and then we exploit this link to develop an algorithm to compute a simplicial complex model of the configuration space. We show how this approach improves the performances of existing algorithms that build other types of representations of the configuration space. The proposed model can be computed in a fraction of the time required to build traditional homotopy-augmented graphs, and is continuous, allowing to solve the path planning task for tethered robots using a broad set of path planning algorithms.

cs.RO↗

arXiv2025

Model Predictive Control for Cooperative Docking Between Autonomous Surface Vehicles with Disturbance Rejection

Uncrewed Surface Vehicles (USVs) are a popular and efficient type of marine craft that find application in a large number of water-based tasks. When multiple USVs operate in the same area, they may be required to dock to each other to perform a shared task. Existing approaches for the docking between autonomous USVs generally consider one USV as a stationary target, while the second one is tasked to reach the required docking pose. In this work, we propose a cooperative approach for USV-USV docking, where two USVs work together to dock at an agreed location. We use a centralized Model Predictive Control (MPC) approach to solve the control problem, obtaining feasible trajectories that also guarantee constraint satisfaction. Owing to its model-based nature, this approach allows the rejection of disturbances, inclusive of exogenous inputs, by anticipating their effect on the USVs through the MPC prediction model. This is particularly effective in case of almost-stationary disturbances such as water currents. In simulations, we demonstrate how the proposed approach allows for a faster and more efficient docking with respect to existing approaches.

cs.RO↗

arXiv2025

Tractable reformulations of DRO problems over structured optimal transport ambiguity sets

Structuring ambiguity sets in Wasserstein-based distributionally robust optimization (DRO) can improve their statistical properties when the uncertainty consists of multiple independent components. The aim of this paper is to solve stochastic optimization problems with unknown uncertainty when we only have access to a finite set of samples from it. Exploiting strong duality of DRO problems over structured ambiguity sets, we derive tractable reformulations for certain classes of DRO and uncertainty quantification problems. We also derive tractable reformulations for distributionally robust chance-constrained problems. As the complexity of the reformulations may grow exponentially with the number of independent uncertainty components, we employ clustering strategies to obtain informative estimators, which yield problems of manageable complexity. We demonstrate the effectiveness of the theoretical results in a numerical simulation example.

math.OC↗

arXiv2025

Gradient sampling algorithm for subsmooth functions

This paper considers non-smooth optimization problems where we seek to minimize the pointwise maximum of a continuously parameterized family of functions. Since the objective function is given as the solution to a maximization problem, neither its values nor its gradients are available in closed form, which calls for approximation. Our approach hinges upon extending the so-called gradient sampling algorithm, which approximates the Clarke generalized gradient of the objective function at a point by sampling its derivative at nearby locations. This allows us to select descent directions around points where the function may fail to be differentiable and establish algorithm convergence to a stationary point from any initial condition. Our key contribution is to prove this convergence by alleviating the requirement on continuous differentiability of the objective function on an open set of full measure. We further provide assumptions under which a desired convex subset of the decision space is rendered attractive for the iterates of the algorithm.

math.OC↗

arXiv2024

Reduced-Order Modelling and Closed-Loop Control of the Cylinder Wake

We present a model-based approach for the closed-loop control of vortex shedding in the cylinder wake. The control objective is to suppress the unsteadiness of the flow, which arises at a critical Reynolds number $Re_c$ through a supercritical Hopf bifurcation. In the vicinity of $Re_c$ the flow is well described by a forced Stuart-Landau equation derived via a global weakly nonlinear analysis. This Stuart-Landau equation governs the evolution of the amplitude $A$ of the global mode on the slow time scale. In this paper, we generalize the approach from [Sipp 2012], which considers a fixed-amplitude harmonic forcing, by allowing the forcing amplitude E0 to vary on the slow time scale. This enables the design of closed-loop controllers for multiple surrogate Stuart-Landau models, which we obtain for different classes of forcing frequencies. When these frequencies are near the global mode oscillation frequency at Rec, we can bring both $A$ and $E'$ to zero, which fully suppresses the unsteady part of the flow. We also show that near this frequency, the optimal forcing structure is in the direction of the adjoint global mode. Assuming partial velocity measurements of the flow, we design an output-feedback control law that stabilizes the flow. The approach hinges on a model predictive controller for the surrogate model, which exploits the full-order model measurements to determine the necessary forcing amplitudes while respecting the modelling constraints. We achieve suppression of the wake oscillations with spatially dense volume forcing and two-point velocity measurement at $Re=50$.

physics.flu-dyn↗

arXiv2024

Data-Driven Strategy Synthesis for Stochastic Systems with Unknown Nonlinear Disturbances

In this paper, we introduce a data-driven framework for synthesis of provably-correct controllers for general nonlinear switched systems under complex specifications. The focus is on systems with unknown disturbances whose effects on the dynamics of the system is nonlinear. The specifications are assumed to be given as linear temporal logic over finite traces (LTLf) formulas. Starting from observations of either the disturbance or the state of the system, we first learn an ambiguity set that contains the unknown distribution of the disturbances with a user-defined confidence. Next, we construct a robust Markov decision process (RMDP) as a finite abstraction of the system. By composing the RMDP with the automaton obtained from the LTLf formula and performing optimal robust value iteration on the composed RMDP, we synthesize a strategy that yields a high probability that the uncertain system satisfies the specifications. Our empirical evaluations on systems with a wide variety of disturbances show that the strategies synthesized with our approach lead to high satisfaction probabilities and validate the theoretical guarantees.

eess.SY↗

arXiv2024

Entanglement Definitions for Tethered Robots: Exploration and Analysis

In this article we consider the problem of tether entanglement for tethered mobile robots. One of the main risks of using a tethered connection between a mobile robot and an anchor point is that the tether may get entangled with the obstacles present in the environment or with itself. To avoid these situations, a non-entanglement constraint can be considered in the motion planning problem for tethered robots. This constraint is typically expressed as a set of specific tether configurations that must be avoided. However, the literature lacks a generally accepted definition of entanglement, with existing definitions being limited and partial in the sense that they only focus on specific instances of entanglement. In practice, this means that the existing definitions do not effectively cover all instances of tether entanglement. Our goal in this article is to bridge this gap and to provide new definitions of entanglement, which, together with the existing ones, can be effectively used to qualify the entanglement state of a tethered robot in diverse situations. The new definitions find application in motion planning for tethered robots, where they can be used to obtain more safe and robust entanglement-free trajectories.

cs.RO↗

arXiv2023

Structured ambiguity sets for distributionally robust optimization

Distributionally robust optimization (DRO) incorporates robustness against uncertainty in the specification of probabilistic models. This paper focuses on mitigating the curse of dimensionality in data-driven DRO problems with optimal transport ambiguity sets. By exploiting independence across lower-dimensional components of the uncertainty, we construct structured ambiguity sets that exhibit a faster shrinkage as the number of collected samples increases. This narrows down the plausible models of the data-generating distribution and mitigates the conservativeness that the decisions of DRO problems over such ambiguity sets may face. We establish statistical guarantees for these structured ambiguity sets and provide dual reformulations of their associated DRO problems for a wide range of objective functions. The benefits of the approach are demonstrated in a numerical example.

math.OC↗

arXiv2022

Efficient Strategy Synthesis for Switched Stochastic Systems with Distributional Uncertainty

We introduce a framework for the control of discrete-time switched stochastic systems with uncertain distributions. In particular, we consider stochastic dynamics with additive noise whose distribution lies in an ambiguity set of distributions that are $\varepsilon-$close, in the Wasserstein distance sense, to a nominal one. We propose algorithms for the efficient synthesis of distributionally robust control strategies that maximize the satisfaction probability of reach-avoid specifications with either a given or an arbitrary (not specified) time horizon, i.e., unbounded-time reachability. The framework consists of two main steps: finite abstraction and control synthesis. First, we construct a finite abstraction of the switched stochastic system as a \emph{robust Markov decision process} (robust MDP) that encompasses both the stochasticity of the system and the uncertainty in the noise distribution. Then, we synthesize a strategy that is robust to the distributional uncertainty on the resulting robust MDP. We employ techniques from optimal transport and stochastic programming to reduce the strategy synthesis problem to a set of linear programs, and propose a tailored and efficient algorithm to solve them. The resulting strategies are correctly refined into switching strategies for the original stochastic system. We illustrate the efficacy of our framework on various case studies comprising both linear and non-linear switched stochastic systems.

eess.SY↗

arXiv2021

High-Confidence Data-Driven Ambiguity Sets for Time-Varying Linear Systems

This paper builds Wasserstein ambiguity sets for the unknown probability distribution of dynamic random variables leveraging noisy partial-state observations. The constructed ambiguity sets contain the true distribution of the data with quantifiable probability and can be exploited to formulate robust stochastic optimization problems with out-of-sample guarantees. We assume the random variable evolves in discrete time under uncertain initial conditions and dynamics, and that noisy partial measurements are available. All random elements have unknown probability distributions and we make inferences about the distribution of the state vector using several output samples from multiple realizations of the process. To this end, we leverage an observer to estimate the state of each independent realization and exploit the outcome to construct the ambiguity sets. We illustrate our results in an economic dispatch problem involving distributed energy resources over which the scheduler has no direct control.

math.OC↗

arXiv2020

Finite Horizon Discrete Models for Multi-Agent Control Systems with Coupled Dynamics

The goal of this paper is to obtain online abstractions for coupled multi-agent systems in a decentralized manner. A discrete model which captures the motion capabilities of each agent is derived over a bounded time-horizon, by discretizing a corresponding overapproximation of the agent's reachable states. The individual abstractions' composition provides a correct representation of the coupled continuous system over the horizon and renders the approach appropriate for control synthesis under high-level specifications which are assigned to the agents over this time window. Sufficient conditions are also provided for the space and time discretization to guarantee the derivation of deterministic abstractions with tunable transition capabilities.

eess.SY↗

arXiv2020

Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs

Ambiguity sets of probability distributions are used to hedge against uncertainty about the true probabilities of random quantities of interest (QoIs). When available, these ambiguity sets are constructed from both data (collected at the initial time and along the boundaries of the physical domain) and concentration-of-measure results on the Wasserstein metric. To propagate the ambiguity sets into the future, we use a physics-dependent equation governing the evolution of cumulative distribution functions (CDF) obtained through the method of distributions. This study focuses on the latter step by investigating the spatio-temporal evolution of data-driven ambiguity sets and their associated guarantees when the random QoIs they describe obey hyperbolic partial-differential equations with random inputs. For general nonlinear hyperbolic equations with smooth solutions, the CDF equation is used to propagate the upper and lower envelopes of pointwise ambiguity bands. For linear dynamics, the CDF equation allows us to construct an evolution equation for tighter ambiguity balls. We demonstrate that, in both cases, the ambiguity sets are guaranteed to contain the true (unknown) distributions within a prescribed confidence.

math.OC↗

arXiv2019

Data-driven ambiguity sets with probabilistic guarantees for dynamic processes

Distributional ambiguity sets provide quantifiable ways to characterize the uncertainty about the true probability distribution of random variables of interest. This makes them a key element in data-driven robust optimization by exploiting high-confidence guarantees to hedge against uncertainty. This paper explores the construction of Wasserstein ambiguity sets in dynamic scenarios where data is collected progressively and may only reveal partial information about the unknown random variable. For random variables evolving according to known dynamics, we leverage assimilated samples to make inferences about their unknown distribution at the end of the sampling horizon. Under exact knowledge of the flow map, we provide sufficient conditions that relate the growth of the trajectories with the sampling rate to establish a reduction of the ambiguity set size as the horizon increases. Further, we characterize the exploitable sample history that results in a guaranteed reduction of ambiguity sets under errors in the computation of the flow and when the dynamics is subject to bounded unknown disturbances. Our treatment deals with both full- and partial-state measurements and, in the latter case, exploits the sampled-data observability properties of linear time-varying systems under irregular sampling. Simulations on a UAV detection application show the superior performance resulting from the proposed dynamic ambiguity sets.

math.OC↗