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20212026
most citedCARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization

9 citations · 20 across the 16 of their papers we have counts for

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8 papers · 1 filter

cs.RO2026

A Generalized Voronoi Graph based Coverage Control Approach for Non-Convex Environment

Zuyi Guo, Ronghao Zheng, Meiqin Liu +1

To address the challenge of efficient coverage by multi-robot systems in non-convex regions with multiple obstacles, this paper proposes a coverage control method based on the Gene…

cs.RO2025

Balanced Collaborative Exploration via Distributed Topological Graph Voronoi Partition

Tianyi Ding, Ronghao Zheng, Senlin Zhang +1

This work addresses the collaborative multi-robot autonomous online exploration problem, particularly focusing on distributed exploration planning for dynamically balanced explorat…

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…

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…