10 citations · 18 across the 8 of their papers we have counts for
8 papers
Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales
Yang Li, Feng Xue, Fan Mo +6
Deploying robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address the…
Intelligent driving vehicle front multi-target tracking and detection based on YOLOv5 and point cloud 3D projection
Dayong Liu, Qingrui Zhang, Zeyang Meng
In multi-target tracking and detection tasks, it is necessary to continuously track multiple targets, such as vehicles, pedestrians, etc. To achieve this goal, the system must be a…
AT-Drone: Benchmarking Adaptive Teaming in Multi-Drone Pursuit
Yang Li, Junfan Chen, Feng Xue +5
Adaptive teaming-the capability of agents to effectively collaborate with unfamiliar teammates without prior coordination-is widely explored in virtual video games but overlooked i…
HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit
Yang Li, Dengyu Zhang, Junfan Chen +4
Zero-shot coordination (ZSC) is a significant challenge in multi-agent collaboration, aiming to develop agents that can coordinate with unseen partners they have not encountered be…
Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation
Yujie Tang, Liang Hu, Qingrui Zhang +1
Inertial measurement units are widely used in different fields to estimate the attitude. Many algorithms have been proposed to improve estimation performance. However, most of them…
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control
Qingrui Zhang, Hao Dong, Wei Pan
Decentralized multi-agent control has broad applications, ranging from multi-robot cooperation to distributed sensor networks. In decentralized multi-agent control, systems are com…
Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles
Qingrui Zhang, Wei Pan, Vasso Reppa
This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance.…
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties
Qingrui Zhang, Wei Pan, Vasso Reppa
This paper presents a novel model-reference reinforcement learning control method for uncertain autonomous surface vehicles. The proposed control combines a conventional control me…