arXiv subjects

Mingxi Zhou

Publications and source records attributed to Mingxi Zhou.

5 recordsLinked to original sources

arXiv2025

Towards Modular and Accessible AUV Systems

This paper reports the development of a new open-access modular framework, called Marine Vehicle Packages (MVP), for Autonomous Underwater Vehicles. The framework consists of both software and hardware designs allowing easy construction of AUV for research with increased customizability and sufficient payload capacity. This paper will present the scalable hardware system design and the modular software design architecture. New features, such as articulated thruster integration and high-level Graphic User Interface will be discussed. Both simulation and field experiments results are shown to highlight the performance and compatibility of the MVP.

cs.RO↗

arXiv2025

A Novel Adaptive Formation Control Strategy for Teams of Unmanned Vehicles Under Complete Dynamic Uncertainty

Modern unmanned systems, including aerial, terrestrial, and underwater vehicles, are increasingly utilized in dynamic and unpredictable environments, where the presence of modeling uncertainties necessitates the development of robust and adaptive control strategies. In this work, we address the formation control problem for a team of unmanned systems with completely uncertain dynamics under a virtual leader-following framework. We propose a novel cooperative adaptive formation control algorithm, designed using artificial neural networks to achieve accurate formation tracking. The effectiveness of the proposed control strategy is established through rigorous theoretical analysis, which guarantees uniform ultimate boundedness of the overall system and exponential convergence of the tracking errors to a small neighborhood of zero. Numerical simulations further validate the effectiveness of the proposed formation control algorithm, demonstrating that the followers accurately track the desired formation trajectory relative to the leader, even in the presence of complete system uncertainties. This work suggests potential application in coordinating multiple unmanned airships for tasks such as persistent aerial surveillance, atmospheric data collection, and wide-area communication support, where adaptability to time-varying and uncertain dynamics is essential.

eess.SY↗

arXiv2024

Adaptive Formation Learning Control for Cooperative AUVs under Complete Uncertainty

This paper presents a two-layer control framework for Autonomous Underwater Vehicles (AUVs) designed to handle uncertain nonlinear dynamics, including the mass matrix, previously assumed known. Unlike prior studies, this approach makes the controller independent of the robot's configuration and varying environmental conditions. The proposed framework applies across different environmental conditions affecting AUVs. It features a first-layer cooperative estimator and a second-layer decentralized deterministic learning controller. This architecture supports robust operation under diverse underwater scenarios, managing environmental effects like changes in water viscosity and flow, which impact the AUV's effective mass and damping dynamics. The first-layer estimator enables seamless inter-agent communication by sharing crucial system estimates without relying on global information. The second-layer controller uses local feedback to adjust each AUV's trajectory, ensuring accurate formation control and dynamic adaptability. Radial basis function neural networks enable local learning and knowledge storage, allowing AUVs to efficiently reapply learned dynamics after system restarts. Simulations validate the effectiveness of this framework, marking it as a significant advancement in distributed adaptive control systems for AUVs, enhancing operational flexibility and resilience in unpredictable marine environments.

eess.SY↗

arXiv2024

Composite Distributed Learning and Synchronization of Nonlinear Multi-Agent Systems with Complete Uncertain Dynamics

This paper addresses the problem of composite synchronization and learning control in a network of multi-agent robotic manipulator systems with heterogeneous nonlinear uncertainties under a leader-follower framework. A novel two-layer distributed adaptive learning control strategy is introduced, comprising a first-layer distributed cooperative estimator and a second-layer decentralized deterministic learning controller. The first layer is to facilitate each robotic agent's estimation of the leader's information. The second layer is responsible for both controlling individual robot agents to track desired reference trajectories and accurately identifying/learning their nonlinear uncertain dynamics. The proposed distributed learning control scheme represents an advancement in the existing literature due to its ability to manage robotic agents with completely uncertain dynamics including uncertain mass matrices. This allows the robotic control to be environment-independent which can be used in various settings, from underwater to space where identifying system dynamics parameters is challenging. The stability and parameter convergence of the closed-loop system are rigorously analyzed using the Lyapunov method. Numerical simulations validate the effectiveness of the proposed scheme.

cs.MA↗

arXiv2023

Tightly-coupled Visual-DVL-Inertial Odometry for Robot-based Ice-water Boundary Exploration

Robotic underwater systems, e.g., Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), are promising tools for collecting biogeochemical data at the ice-water interface for scientific advancements. However, state estimation, i.e., localization, is a well-known problem for robotic systems, especially, for the ones that travel underwater. In this paper, we present a tightly-coupled multi-sensors fusion framework to increase localization accuracy that is robust to sensor failure. Visual images, Doppler Velocity Log (DVL), Inertial Measurement Unit (IMU) and Pressure sensor are integrated into the state-of-art Multi-State Constraint Kalman Filter (MSCKF) for state estimation. Besides that a new keyframe-based state clone mechanism and a new DVL-aided feature enhancement are presented to further improve the localization performance. The proposed method is validated with a data set collected in the field under frozen ice, and the result is compared with 6 other different sensor fusion setups. Overall, the result with the keyframe enabled and DVL-aided feature enhancement yields the best performance with a Root-mean-square error of less than 2 m compared to the ground truth path with a total traveling distance of about 200 m.

cs.RO↗