Publications

First-Author Publications

Submitted Preprint Manuscript

A Dynamically Weighted ADMM Framework for Byzantine ResilienceThe alternating direction of multipliers method (ADMM) is a popular method to solve distributed consensus optimization utilizing efficient communication among various nodes in the network. However, in the presence of faulty or attacked nodes, even a small perturbation (or sharing false data) during the communication can lead to divergence of the solution. To address this issue, in this work we consider ADMM under the effect of Byzantine threat, where an unknown subset of nodes is subject to Byzantine attacks or faults. We propose Dynamically Weighted ADMM (DW-ADMM), a novel variant of ADMM that uses dynamic weights on the edges of the network, thus promoting resilient distributed optimization. We establish that the proposed method (i) produces a nearly identical solution to conventional ADMM in the error-free case, and (ii) guarantees a bounded solution with respect to the global minimizer, even under Byzantine threat. Finally, we demonstrate the effectiveness of our proposed algorithm using an illustrative numerical simulation.

Published Manuscript

Range-Based Multi-Robot Integrity Monitoring For Cyberattacks and Faults: An Anchor-Free ApproachCoordination of multi-robot systems (MRSs) relies on efficient sensing and reliable communication among the robots. However, the sensors and communication channels of these robots are often vulnerable to cyberattacks and faults, which can disrupt their individual behavior and the overall objective of the MRS. In this work, we present a multi-robot integrity monitoring framework that utilizes inter-robot range measurements to (i) detect the presence of cyberattacks or faults affecting the MRS, (ii) identify the affected robot(s), and (iii) reconstruct the resulting localization error of these robot(s). The proposed iterative algorithm leverages sequential convex programming and alternating direction of multipliers method to enable real-time and distributed implementation. Our approach is validated using numerical simulations and demonstrated using PX4-SiTL in Gazebo on an MRS, where certain agents deviate from their desired position due to a GNSS spoofing attack. Furthermore, we demonstrate the scalability and interoperability of our algorithm through mixed-reality experiments by forming a heterogeneous MRS comprising real Crazyflie UAVs and virtual PX4-SiTL UAVs working in tandem.

Other Publications

Submitted Preprint Manuscript

On Enhancing Structural Resilience of Multirobot Coverage Control with Bearing RigidityThe problem of multi-robot coverage control has been widely studied to efficiently coordinate a team of robots to cover a desired area of interest. However, this problem faces significant challenges when some robots are lost or deviate from their desired formation during the mission due to faults or cyberattacks. Since a majority of multi-robot systems (MRSs) rely on communication and relative sensing for their efficient operation, a failure in one robot could result in a cascade of failures in the entire system. In this work, we propose a hierarchical framework for area coverage, combining centralized coordination by leveraging Voronoi partitioning with decentralized reference tracking model predictive control (MPC) for control design. In addition to reference tracking, the decentralized MPC also performs bearing maintenance to enforce a rigid MRS network, thereby enhancing the structural resilience, i.e., the ability to detect and mitigate the effects of localization errors and robot loss during the mission. Furthermore, we show that the resulting control architecture guarantees the recovery of the MRS network in the event of robot loss while maintaining a minimally rigid structure. The effectiveness of the proposed algorithm is validated through numerical simulations.

Published Manuscript

Data-Driven Reachability Analysis for Nonlinear SystemsWe consider the problem of forward reachability analysis of a closed-box nonlinear system, using only the data from the system. We propose a method that computes an ellipsoidal set that tightly over-approximates the true reachable set using convex optimization. Exploiting the fact that a linear approximation of a nonlinear system is not unique, we find conditions of a linear time-varying system that approximates the nonlinear system such that its reachable set is guaranteed to include the reachable set of the unknown nonlinear system, assuming that the Lipchitz coefficient of the nonlinear system is known. Then, we formulate a convex optimization problem that jointly searches for the parameters of the linear system and its ellipsoidal over-approximate reachable set based only on the data to minimize the growth rate of the reachable set while ensuring the ellipsoid over-approximates the true reachable set. We demonstrate the advantages of the proposed method via two illustrative examples: an autonomous nonlinear system and the TRAF22 benchmark system, and compare the results with other state-of-the-art algorithms.

Published Manuscript

On The Controllability Preservation of Koopman Bilinear Surrogate ModelIn this paper, we analyze the controllability of the Koopman bilinear surrogate model of a controllable control affine system. The Koopman operator is a linear operator that can describe the evolution of an original (nonlinear) system by lifting the state using an observable. However, it has been proven that the lifted system may not necessarily be fullstate controllable even if the original system is. Moreover, the infinite-dimensional nature of the Koopman operator means that a finite-dimensional approximation is often required in practice and thus, one cannot simply guarantee the lifted system to preserve the same controllability property of the original system. Motivated by this, we investigate how the controllability property of the original system affects that of the lifted system. We specifically focus on control affine systems, where one can construct a Koopman bilinear surrogate model using the infinitesimal generator of the Koopman operator. We assume there exists an admissible controller that can drive the state of the original control affine system to a desired state. Then, we present the controllability property of the corresponding Koopman bilinear surrogate model, constructed by the data-driven infinitesimal generator using generator extended dynamic mode decomposition (gEDMD). A numerical simulation example using a quadrotor model is presented to demonstrate the proposed results.

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