most citedARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2024

HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks

Jingsong Liang, Yuhong Cao, Yixiao Ma +2

In this paper, we introduce HDPlanner, a deep reinforcement learning (DRL) based framework designed to tackle two core and challenging tasks for mobile robots: autonomous explorati…

cs.RO2024

Privileged Reinforcement and Communication Learning for Distributed, Bandwidth-limited Multi-robot Exploration

Yixiao Ma, Jingsong Liang, Yuhong Cao +2

Communication bandwidth is an important consideration in multi-robot exploration, where information exchange among robots is critical. While existing methods typically aim to reduc…

cs.RO2024

STAR: Swarm Technology for Aerial Robotics Research

Jimmy Chiun, Yan Rui Tan, Yuhong Cao +2

In recent years, the field of aerial robotics has witnessed significant progress, finding applications in diverse domains, including post-disaster search and rescue operations. Des…

cs.RO20231 cited

Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets

Yizhuo Wang, Yutong Wang, Yuhong Cao +1

This work focuses on the persistent monitoring problem, where a set of targets moving based on an unknown model must be monitored by an autonomous mobile robot with a limited sensi…

cs.RO20233 cited

ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration

Yuhong Cao, Tianxiang Hou, Yizhuo Wang +2

In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible. Since the environment is being revealed during e…