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This paper considers the problem of optimizing robot navigation with respect to a time-varying objective encoded into a navigation density function. We are interested in designing state feedback control laws that lead to an almost everywhere stabilization of the closed-loop system to an equilibrium point while navigating a region optimally and safely (that is, the transient leading to the final equilibrium point is optimal and satisfies safety constraints). Though this problem has been studied in literature within many different communities, it still remains a challenging non-convex control problem. In our approach, under certain assumptions on the time-varying navigation density, we use Koopman and Perron-Frobenius Operator theoretic tools to transform the problem into a convex one in infinite dimensional decision variables. In particular, the cost function and the safety constraints in the transformed formulation become linear in these functional variables. Finally, we present some numerical examples to illustrate our approach, as well as discuss the current limitations and future extensions of our framework to accommodate a wider range of robotics applications., Part of ISBN 978-3-907144-08-4QC 20230911

This paper proposes a high-level path planning strategy under Time Petri net (TPN) formalism for a multi-agent system, which is subject to Metric Interval Temporal Logic (MITL) specifications. The work aims to design a scalable model with respect to the number of agents, as the MITL formula requires multiple agents to ensure similar tasks. The obtained model is denoted Composed Time Petri net and it couples two TPN representations assigned to the motion of the agents, respectively to the MITL specification. The planning approach is based on model-checking methods and the results are evaluated on a case study applied in robotics industry., Part of ISBN 978-3-907144-08-4QC 20230911

This paper proposes a method to navigate a mobile robot by estimating its state over a number of distributed sensor networks (DSNs) such that it can successively accomplish a sequence of tasks, i.e., its state enters each targeted set and stays inside no less than the desired time, under a resource-aware, time-efficient, and computation- and communication-constrained setting. We propose a new robot state estimation and navigation architecture, which integrates an event-triggered task-switching feedback controller for the robot and a two-time-scale distributed state estimator for each sensor. With the controller, the robot is able to accomplish a task by following a reference trajectory and switch to the next task when an event-triggered condition is fulfilled. With the estimator, each active sensor is able to estimate the robot state. We provide conditions to ensure that the state estimation error and the trajectory tracking deviation are upper bounded by two time-varying sequences, respectively. Furthermore, we find a sufficient condition for accomplishing a task and provide an upper bound of running time for the task. Numerical simulations of an indoor robot's localization and navigation are provided to validate the proposed architecture., Part of ISBN 978-3-907144-08-4QC 20230911

In this paper, we consider a planning problem for a hierarchical finite state machine (HFSM) and develop an algorithm for efficiently computing optimal plans between any two states. The algorithm consists of an offline and an online step. In the offline step, one computes exit costs for each machine in the HFSM. It needs to be done only once for a given HFSM, and it is shown to have time complexity scaling linearly with the number of machines in the HFSM. In the online step, one computes an optimal plan from an initial state to a goal state, by first reducing the HFSM (using the exit costs), computing an optimal trajectory for the reduced HFSM, and then expand this trajectory to an optimal plan for the original HFSM. The time complexity is near-linearly with the depth of the HFSM. It is argued that HFSMs arise naturally for large-scale control systems, exemplified by an application where a robot moves between houses to complete tasks. We compare our algorithm with Dijkstra's algorithm on HFSMs consisting of up to 2 million states, where our algorithm outperforms the latter, being several orders of magnitude faster., Part of ISBN 978-3-907144-08-4QC 20230911

This paper considers distributed nonconvex optimization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels often have limited bandwidth or capacity, we first introduce a quantization rule and an encoder/decoder scheme to reduce the transmission bits. By integrating them with a distributed algorithm, we then propose a distributed quantized nonconvex optimization algorithm. Assuming the global cost function satisfies the Polyak-Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that the proposed algorithm linearly converges to a global optimal point. Moreover, a low data rate is shown to be sufficient to ensure linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical simulation examples., QC 20230503

Control of multi-agent systems under temporal logical specifications has been popular due to its ability to tackle complex tasks that cannot be easily defined as classic control objectives. In this paper, a general class of leader-follower multi-agent systems subject to certain fragments of signal temporal logic (STL) specifications is considered. We first propose a funnel-based control strategy for the leader-follower multi-agent systems to enforce the satisfaction of the basic STL formulas by prescribing certain transient behavior on the funnels that constrain the closed-loop trajectories. A hybrid control strategy is then leveraged to satisfy the sequential STL formulas. Finally, a simulation example is given to illustrate the results., Part of proceeedings: ISBN 978-3-907144-07-7QC 20220621

This paper proposes a data-driven set-based estimation algorithm for a class of nonlinear systems with polynomial nonlinearities. Using the system's input-output data, the proposed method computes a set that guarantees the inclusion of the system's state in real-time. Although the system is assumed to be a polynomial type, the exact polynomial functions, and their coefficients are assumed to be unknown. To this end, the estimator relies on offline and online phases. The offline phase utilizes past input-output data to estimate a set of possible coefficients of the polynomial system. Then, using this estimated set of coefficients and the side information about the system, the online phase provides a set estimate of the state. Finally, the proposed methodology is evaluated through its application on SIR (Susceptible, Infected, Recovered) epidemic model., Part of proceedings: ISBN 978-3-907144-07-7, QC 20221101

In this paper, we develop and analyze a gossip-based average consensus algorithm that enables all of the components of a distributed system, each with some initial value, to reach (approximate) average consensus on their initial values after executing a finite number of iterations, and without having to reveal to other curious components the specific value they contribute to the average calculation. We consider a fully-connected (undirected) network in which curious components do not interfere in the computation in any other way, but can collaborate arbitrarily and are aware of the privacy-preserving strategy. We characterize precisely conditions on the information exchange that guarantee privacy preservation for a specific node. The protocol also provides a criterion that allows the nodes to determine, in a distributed manner (while running the proposed gossip protocol), when to terminate their operation because approximate average consensus has been reached., Part of proceedings: ISBN 978-3-907144-07-7, QC 20221101

Control of multi-agent systems under temporal logical specifications has been popular due to its ability to tackle complex tasks that cannot be easily defined as classic control objectives. In this paper, a general class of leader-follower multi-agent systems subject to certain fragments of signal temporal logic (STL) specifications is considered. We first propose a funnel-based control strategy for the leader-follower multi-agent systems to enforce the satisfaction of the basic STL formulas by prescribing certain transient behavior on the funnels that constrain the closed-loop trajectories. A hybrid control strategy is then leveraged to satisfy the sequential STL formulas. Finally, a simulation example is given to illustrate the results., Part of proceeedings: ISBN 978-3-907144-07-7QC 20220621

The paper studies the spread of a virus over a (possibly) time-varying graph, with the spread being (possibly) worsened by the presence of a shared resource. We propose a time-varying susceptible-infected-water-susceptible (SIWS) model, with the water compartment representing the contamination level in the shared resource. We say that the system is in the disease-free equilibrium (DFE) if none of the nodes (representative of sub-populations, such as cities, districts, etc.) are infected, and the shared resource is contaminant-free. We identify multiple sufficient conditions for exponential convergence to the DFE. Based on one of the aforementioned sufficient conditions, an on/off lockdown strategy that eradicates the infection spread is proposed. More specifically, we design a switching rule between lockdown and free (i.e., no lockdown) modes to guarantee exponential convergence to the DFE., Part of proceedings: ISBN 978-3-907144-07-7Not duplicate with DiVA 1622798QC 20221031

Control of multi-agent systems under temporal logical specifications has been popular due to its ability to tackle complex tasks that cannot be easily defined as classic control objectives. In this paper, a general class of leader-follower multi-agent systems subject to certain fragments of signal temporal logic (STL) specifications is considered. We first propose a funnel-based control strategy for the leader-follower multi-agent systems to enforce the satisfaction of the basic STL formulas by prescribing certain transient behavior on the funnels that constrain the closed-loop trajectories. A hybrid control strategy is then leveraged to satisfy the sequential STL formulas. Finally, a simulation example is given to illustrate the results., Part of proceeedings: ISBN 978-3-907144-07-7QC 20220621

Control of multi-agent systems under temporal logical specifications has been popular due to its ability to tackle complex tasks that cannot be easily defined as classic control objectives. In this paper, a general class of leader-follower multi-agent systems subject to certain fragments of signal temporal logic (STL) specifications is considered. We first propose a funnel-based control strategy for the leader-follower multi-agent systems to enforce the satisfaction of the basic STL formulas by prescribing certain transient behavior on the funnels that constrain the closed-loop trajectories. A hybrid control strategy is then leveraged to satisfy the sequential STL formulas. Finally, a simulation example is given to illustrate the results., Part of proceeedings: ISBN 978-3-907144-07-7QC 20220621

Control of multi-agent systems under temporal logical specifications has been popular due to its ability to tackle complex tasks that cannot be easily defined as classic control objectives. In this paper, a general class of leader-follower multi-agent systems subject to certain fragments of signal temporal logic (STL) specifications is considered. We first propose a funnel-based control strategy for the leader-follower multi-agent systems to enforce the satisfaction of the basic STL formulas by prescribing certain transient behavior on the funnels that constrain the closed-loop trajectories. A hybrid control strategy is then leveraged to satisfy the sequential STL formulas. Finally, a simulation example is given to illustrate the results., Part of proceeedings: ISBN 978-3-907144-07-7QC 20220621

Designing Luenberger observers for nonlinear systems involves the challenging task of transforming the state to an alternate coordinate system, possibly of higher dimensions, where the system is asymptotically stable and linear up to output injection. The observer then estimates the system's state in the original coordinates by inverting the transformation map. However, finding a suitable injective transformation whose inverse can be derived remains a primary challenge for general nonlinear systems. We propose a novel approach that uses supervised physics-informed neural networks to approximate both the transformation and its inverse. Our method exhibits superior generalization capabilities to contemporary methods and demonstrates robustness to both neural network's approximation errors and system uncertainties., Part of ISBN 979-8-3503-2806-6QC 20230922

We consider the problem of learning functions by two agents and a fusion center from noisy data. True data comprises of samples of an independent variable (input) and the corresponding value of a dependent variable (output) collectively labeled as (input, output) data. The data received by the agents, both the input and output data, are corrupted by noise. The objective of the agents is to learn a mapping from the true input to the true output. We formulate a general regression problem for the agents followed by the least squares regression (LS) problem. We prove a stochastic representer theorem for the general regression problem and subsequently solve the LS problem. The functions learned by the agents are transmitted to the fusion center where an optimization problem is formulated to fuse the functions together, which is then declared as the mapping. As an example, the methodology developed has been applied to the data generated from a transcendental function., Part of ISBN 978-3-907144-08-4QC 20230911

For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns naturally arise from disclosing sensitive measurement signals to a cloud estimator that predicts the system state. To solve this issue, we propose a differentially private set-based estimation protocol that preserves the privacy of the measurement signals. Compared to existing research, our approach achieves less privacy loss and utility loss using a numerically optimized truncated noise distribution. The proposed estimator is perturbed by weaker noise than the analytical approaches in the literature to guarantee the same level of privacy, therefore improving the estimation utility. Numerical and comparison experiments with truncated Laplace noise are presented to support our approach. Zonotopes, a less conservative form of set representation, are used to represent estimation sets, giving set operations a computational advantage. The privacy-preserving noise anonymizes the centers of these estimated zonotopes, concealing the precise positions of the estimated zonotopes., Part of ISBN 978-3-907144-08-4QC 20230911

Reinforcement Learning aims at identifying and evaluating efficient control policies from data. In many real-world applications, the learner is not allowed to experiment and cannot gather data in an online manner (this is the case when experimenting is expensive, risky or unethical). For such applications, the reward of a given policy (the target policy) must be estimated using historical data gathered under a different policy (the behavior policy). Most methods for this learning task, referred to as Off-Policy Evaluation (OPE), do not come with accuracy and certainty guarantees. We present a novel OPE method based on Conformal Prediction that outputs an interval containing the true reward of the target policy with a prescribed level of certainty. The main challenge in OPE stems from the distribution shift due to the discrepancies between the target and the behavior policies. We propose and empirically evaluate different ways to deal with this shift. Some of these methods yield conformalized intervals with reduced length compared to existing approaches, while maintaining the same certainty level., QC 20230811

The privacy aspect of state estimation algorithms has been drawing high research attention due to the necessity for a trustworthy private environment in cyber-physical systems. These systems usually engage cloud-computing platforms to aggregate essential information from spatially distributed nodes and produce desired estimates. The exchange of sensitive data among semi-honest parties raises privacy concerns, especially when there are coalitions between parties. We propose two privacy-preserving protocols using Kalman filter and partially homomorphic encryption of the measurements and estimates while exposing the covariances and other model parameters. We prove that the proposed protocols achieve satisfying computational privacy guarantees against various coalitions based on formal cryptographic definitions of indistinguishability. We evaluate the proposed protocols to demonstrate their efficiency using data from a real testbed., Part of proceedings: ISBN 978-3-907144-07-7, QC 20221101

We propose a distributed control law that solves the tracking-in-formation problem for a group of underactuated autonomous marine vehicles interconnected over an undirected graph and subject to inter-agent collision-avoidance and connectivity constraints. The control approach is based on input-output feedback linearization using the so-called hand-position point as the output. Moreover, the control strategy is able to deal with limited knowledge on the target's state and dynamics as well as with disturbances in the form of unknown irrotational ocean currents. We establish almost-everywhere uniform asymptotic stability of the output dynamics with guaranteed respect of the inter-agent constraints. A numerical simulation illustrates the effectiveness of the proposed approach., Part of proceedings: ISBN 978-3-907144-07-7, QC 20221101

Connected and Automated Vehicles (CAVs) are likely to have a large impact on the traffic in the near future. Assuming we are able to communicate some commands directly to them, it is of interest to know how CAVs can be used for traffic control. In order to achieve this, we need to understand how such controls affect the rest of the traffic. In this work, we study the influence of a CAV acting as a moving bottleneck, using the CAV's speed as a control input. We discuss the interpretation of the microscopic traffic data in the macroscopic framework, and propose nonparametric methods for learning the micro-macro model describing the interaction between the CAV and the surrounding traffic. We use only the local traffic data in the vicinity of the CAV, and design simple, targeted data collection experiments. This learned model is then used to predict the evolution of the traffic, and the predictions are compared with corresponding data from microscopic simulations., Part of proceedings: ISBN 978-3-907144-07-7QC 20221031

In this contribution we consider sparse linear regression problems. It is well known that the mutual coherence, i.e. the maximum correlation of the regressors, is important for the ability of any algorithm to recover the sparsity pattern of an unknown parameter vector from data. A low mutual coherence improves the ability of recovery. In optimal experiment design this requirement may be in conflict with other objectives encoded by the desired Fisher matrix. In this contribution we alleviate this issue by combining optimal input design with a recently proposed approach to achieve low mutual coherence by way of a linear coordinate transformation. The resulting optimization problem is solved using cyclic minimization. Via simulations we demonstrate that the resulting algorithm is able to achieve a Fisher matrix which results in a performance close to the performance if the sparsity would have been known, while at the same time being able to recover the sparsity pattern., Part of proceedings; ISBN 978-3-907144-07-7QC 20221031

We propose a method to perform set-based state estimation of an unknown dynamical linear system using a data-driven set propagation function. Our method comes with set-containment guarantees, making it applicable to safety-critical systems. The method consists of two phases: (1) an offline learning phase where we collect noisy input-output data to determine a function to propagate the state-set ahead in time; and (2) an online estimation phase consisting of a time update and a measurement update. It is assumed that known finite sets bound measurement noise and disturbances, but we assume no knowledge of their statistical properties. These sets are described using zonotopes, allowing efficient propagation and intersection operations. We propose a new approach to compute a set of models consistent with the data and noise-bound, given input-output data in the offline phase. The set of models is utilized in replacing the unknown dynamics in the data-driven set propagation function in the online phase. Then, we propose two approaches to perform the measurement update. Simulations show that the proposed estimator yields state sets comparable in volume to the 3 sigma confidence bounds obtained by a Kalman filter approach, but with the addition of state set-containment guarantees. We observe that using constrained zonotopes yields smaller sets but with higher computational costs than unconstrained ones., Part of proceedings: ISBN 978-3-907144-07-7QC 20221031

We study a concentration phenomenon in a gossip model that evolves over a stochastic block model (SBM) with two communities. We study the conditional mean of the stationary distribution of the gossip model over the SBM, and show that it is close to the mean of the stationary distribution of the gossip model over an averaged graph, with high probability. As a consequence, regular (non-stubborn) agents in the same community of the gossip model over the SBM have stationary states with similar expectations. The results show that it is possible to use the gossip model over the averaged graph to approximate and analyze the gossip model over the SBM, and establish a correspondence between agent states and community structure of a network. We present numerical simulations to illustrate the results., Part of proceedings: ISBN 978-3-907144-07-7QC 20221031