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20212023
most citedRobust Inertial-aided Underwater Localization based on Imaging Sonar Keyframes

45 citations · 75 across the 11 of their papers we have counts for

collaborators

11 papers

cs.RO2023★ 1 cited

Five-Tiered Route Planner for Multi-AUV Accessing Fixed Nodes in Uncertain Ocean Environments

Jiaxin Zhang, Meiqin Liu, Senlin Zhang +2

This article introduces a five-tiered route planner for accessing multiple nodes with multiple autonomous underwater vehicles (AUVs) that enables efficient task completion in stoch…

cs.RO2023★ 9 cited

CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization

Yang Xu, Ronghao Zheng, Senlin Zhang +2

In this paper, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) re…

cs.RO2023★ 2 cited

Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression

Tianyi Ding, Ronghao Zheng, Senlin Zhang +1

Cooperative online scalar field mapping is an important task for multi-robot systems. Gaussian process regression is widely used to construct a map that represents spatial informat…

eess.SY2023

Distributed Target Tracking with Fading Channels over Underwater Wireless Sensor Networks

Miaoyi Tang, Meiqin Liu, Senlin Zhang +2

This paper investigates the problem of distributed target tracking via underwater wireless sensor networks (UWSNs) with fading channels. The degradation of signal quality due to wi…

physics.flu-dyn2023

Physics-informed Neural Network Combined with Characteristic-Based Split for Solving Navier-Stokes Equations

Shuang Hu, Meiqin Liu, Senlin Zhang +2

In this paper, physics-informed neural network (PINN) based on characteristic-based split (CBS) is proposed, which can be used to solve the time-dependent Navier-Stokes equations (…

cs.RO2023

Bayesian Generalized Kernel Inference for Exploration of Autonomous Robots

Yang Xu, Ronghao Zheng, Senlin Zhang +1

This paper concerns realizing highly efficient information-theoretic robot exploration with desired performance in complex scenes. We build a continuous lightweight inference model…