2021 International Symposium on Multi-Robot and Multi-Agent Systems MRS

活动简介

IEEE MRS 2021 will be held on November 4-5 at St John’s College, University of Cambridge, UK. We are closely monitoring the global pandemic situation. Currently, we are optimistically planning for an in-person conference in Cambridge, possibly with a remote-live option for those who cannot travel. We will revise these plans as the COVID situation evolves in the coming months.

The goal of the conference is to bring together researchers who are in the field of multi-robot systems (MRS) and multi-agent systems (MAS). Typically MRS/MAS research is spread across large conferences, and this makes it difficult for us to keep up to date on new findings and meet others in the area. The intent of the conference is to bring those researchers together with a high-quality symposium to highlight the very best in the field. MRS is an initiative of the IEEE RAS Technical Committee on Multi-Robot Systems. Previous iterations include MRS 2017, MRS 2019.

Sponsor Type:1; 9

组委会

General Chair

Amanda Prorok, University of Cambridge, UK

Editor in Chief

Mac Schwager, Stanford University, USA

Program Chairs

Paolo Robuffo Giordano, IRISA-CNRS, France
Nora Ayanian, University of Southern California, USA
Chris Amato, Northeastern University, USA
Dan Halperin, Tel Aviv University, Israel

Publications Chair

Javier Alonso Mora, Delft University of Technology, Netherlands

Finance Chair

Xi Yu, University of Pennsylvania, USA

Awards Chair

Kirstin Petersen, Cornell University, USA

Registration Chair

Zhe Liu, University of Cambridge, UK

Website Chair

Jan Blumenkamp, University of Cambridge, UK

征稿信息

重要日期

2021-05-17

初稿截稿日期

2021-07-30

初稿录用日期

征稿范围

The conference scope will include any research related to multi-robot and multi-agent systems, an inherently diverse community. Several competences are needed in this field, ranging from control systems to mechanical design, coordination, cooperation, estimation, perception and interaction. The fields of interest include the following general fields, but are not limited to:

Modeling and Control of MRS/MAS
Discrete Planning, Routing, Multi-Robot Path Finding, Task Assignment
Distributed Optimization and Distributed Algorithms
Nonlinear Control, Lyapunov Methods, Optimal Control
Continuous Planning, Trajectory Optimization, Informative Planning
Deep Learning, Reinforcement Learning, General Learning, General AI
Distributed Perception, Vision, Mapping, SLAM
Estimation, Filtering, Bayesian Methods, Target Tracking
Robustness, Formal Methods, Network Resilience
Human-Multi-Robot Interaction
Micro/Nano Scale Systems
Bio-Inspired Systems and and Swarm Intelligence