6 papers
Learning Efficient Flocking Control based on Gibbs Random Fields
Dengyu Zhang, Chenghao, Feng Xue +1
Flocking control is essential for multi-robot systems in diverse applications, yet achieving efficient flocking in congested environments poses challenges regarding computation bur…
Heuristic Predictive Control for Multi-Robot Flocking in Congested Environments
Guobin Zhu, Qingrui Zhang, Bo Zhu +1
Multi-robot flocking possesses extraordinary advantages over a single-robot system in diverse domains, but it is challenging to ensure safe and optimal performance in congested env…
GRF-based Predictive Flocking Control with Dynamic Pattern Formation
Chenghao Yu, Dengyu Zhang, Qingrui Zhang
It is promising but challenging to design flocking control for a robot swarm to autonomously follow changing patterns or shapes in a optimal distributed manner. The optimal flockin…
DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention
Zheng Zhang, Dengyu Zhang, Qingrui Zhang +2
Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do…
Formation Control for Moving Target Enclosing via Relative Localization
Xueming Liu, Kunda Liu, Tianjiang Hu +1
In this paper, we investigate the problem of controlling multiple unmanned aerial vehicles (UAVs) to enclose a moving target in a distributed fashion based on a relative distance a…
Distributed Flocking Control of Aerial Vehicles Based on a Markov Random Field
Guobin Zhu, Shanwei Fan, Qingrui Zhang
The distributed flocking control of collective aerial vehicles has extraordinary advantages in scalability and reliability, \emph{etc.} However, it is still challenging to design a…