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AIRS in the AIR—Michael Rubenstein and Tin Lun Lam

Tuesday, May 17, 2022 9:00 AM ~ Tuesday, May 17, 2022 11:00 AM
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模块化自重构机器人是近年来的研究热点。它是一种由同构单元组成的机器人系统,可根据不同任务和环境转变为合适的构型,特别适用于工作环境变化大、操作任务复杂的场合,在抢险搜救、反恐侦察、太空探索等领域有着广阔的应用前景。

为了加强科研成果和知识共享,促进本领域的学术交流与合作,助力理论创新和技术发展,AIRS in the AIR 五月系列活动邀请世界顶级学者围绕“模块化自重构机器人”开展讲座。本月讲座将带大家回顾模块化可重构机器人的发展历史,深入探讨目前技术发展面临的挑战与解决方案,并详细介绍如何通过研究,让机器人更好地服务于人类,以及推动社会发展。

*讲座主题5

Taming the Swarm: Scalability in Control and Design of Swarm Robotics/

探索集群:集群机器人控制和设计的扩展性

摘要:

Advances in technology have begun to allow for the production of large groups, or swarms, of robots; however, there exists a large gap between their current capabilities and those of swarms found in nature or envisioned for future robot swarms.  These deficiencies are the result of two factors, difficulties in algorithmic control of these swarms, and limitations in hardware capabilities of the individuals.

Creating a hardware system for large robotic swarms is an open challenge; cost and manufacturability pressure hardware designs to be simple with minimal capabilities, while algorithm design favors more capable hardware. The robot design must balance these factors to create a simple robot that is, at the same time, capable of performing the desired behaviors.  In this talk, I will discuss the many challenges associated with creating a robot swarm at this scale and the implications this has for creating even larger, more capable swarms in the future.

技术的进步使得集群机器人的生产成为可能;然而,它们目前的能力与自然界中发现的或我们畅想的未来集群机器人能力之间存在很大差距。这些缺陷是由两个因素造成的,一是集群算法控制的困难,二是个体硬件能力的限制。

大规模的集群机器人硬件系统是一个公认的挑战:成本和可制造性要求硬件设计尽可能简单以实现功能最小化,而算法设计则要求更强大而复杂的硬件平台。机器人设计必须平衡这些因素,以实现简单并能够执行目标行为的机器人。在这次演讲中,我将讨论实现大规模集群机器人的挑战,以及这对未来创建规模更大、功能更强大的集群机器人可能带来的影响。

讲座嘉宾:

Michael.jpg

Michael Rubenstein is currently an assistant professor at Northwestern's McCormick School of Engineering.  There he is working on Kilobot, a robot designed for testing swarm algorithms in a group of over a thousand robots.  He received his Ph.D. from The University of Southern California's School of Computer Science under the supervision of Wei-Min Shen.  His thesis, titled: "Self-Assembly and Self-Healing for Robotic Collectives", details a control algorithm for a simple, simulated multi-robot system which guarantees that it can self-assemble and self-heal any desired connected shape.  Most of his research is centered around the design and control of multi-robot systems.  Additional information can be found at his webpage:http://users.eecs.northwestern.edu/~mrubenst/

Michael Rubenstein是美国西北大学McCormick工程学院助理教授。他的研究重点专注于Kilobot,一个逾千规模的集群机器人系统。Michael Rubenstein师从沈为民教授,获得了南加州大学计算机学院的博士学位。他的博士论文详细介绍了一个模拟的多机器人系统的控制算法,该系统保证它可以自组装和自修复成任何所需的构型。他的研究兴趣集中在多机器人系统的设计和控制上。更多信息可参考:http://users.eecs.northwestern.edu/~mrubenst/



*讲座主题6

Bio-inspired Freeform Reconfigurable Robot/

生物启发的自由重构机器人

摘要:

​A general-purpose robotic system that can change its configuration to achieve different capabilities is of great value to application scenarios with substantial uncertainty, such as exploration, search and rescue in hazard fields. Inspired by the great flexibility and adaptability of biological clusters such as ants and slime molds in facing dynamic environments, the modular self-reconfigurable robot is one of the promising directions for realizing general-purpose robots. The modular self-reconfigurable robots can form different shapes to achieve different capabilities by the autonomous connection and motion among simple modular robots. Due to the mechanical constraints, modular robots only provide several connecting points for other robots, which is different from the natural biological clusters that can connect anywhere with their fellows. The restricted location of connection highly limited the flexibility and increased the connection complexity of the modular self-reconfigurable robots. Is it possible to get one step closer to the capability of the biological clusters by enabling modular self-reconfigurable robots to have more freedom in forming their shape? This talk will introduce some of our recent attempts at tackling this challenge.

一个可以改变其构型以实现不同功能的通用机器人系统,对于存在不确定性的应用场景具有重要价值,如危险现场的勘探、搜索和救援。受蚂蚁和黏菌等生物群体在面对动态环境时的灵活性和适应性的启发,模块化自重构机器人是实现通用目标机器人的一个有潜力的方向。模块化自重构机器人通过简单的模块化机器人之间的自主连接和运动,可以形成不同的形状,实现不同的功能。大部分模块化机器人由于几何约束只能实现模块间位置和数量有限的连接。这与自然界中生物集群可以在任何姿态下与同伴连接大为不同。受限的连接位置极大地限制了模块化自重构机器人的灵活性,增加了连接的复杂性。模块化自重构机器人更自由的重构,是否有可能接近生物集群的能力?本次演讲将介绍我们最近对这一挑战的一些尝试。

讲座嘉宾:

tinlun lam.png

Tin Lun Lam, Senior Member of IEEE, serves as Assistant Professor of the Chinese University of Hong Kong, Shenzhen, Executive Deputy Director of the National-local Joint Engineering Laboratory of Robotics and Intelligent Manufacturing, and Director of the Center for Intelligent Robots of Shenzhen Institute of Artificial Intelligence and Robotics for Society. He received his B.Eng. Degree with First Class Honors and Ph.D. Degree in Robotics and Automation from the Chinese University of Hong Kong in 2006 and 2010, respectively. The research focus includes multi-robot systems, field robotics, and human-robot collaboration. He has been granted over 60 patents, published 2 monographs, and over 60 international journal and conference papers. Most of them were published in top-tier international journals and conference proceedings in robotics and automation, such as TRO, JFR, T-MECH, RA-L, ICRA, and IROS. Based on his research, he received an IEEE/ASME T-MECH Best Paper Award in 2011 and IROS Best Paper Award on Robot Mechanisms and Design in 2020. His research outcomes are also reported in many popular media, including Reuters, Discovery Channel, IEEE Spectrum, and NHK.

林天麟,IEEE高级会员,香港中文大学(深圳)理工学院助理教授,博士生导师,担任机器人与智能制造国家地方联合工程实验室执行副主任及深圳市人工智能与机器人研究院(AIRS)智能机器人中心主任;师从徐扬生院士,分别于2006年和2010年在香港中文大学获得一等荣誉学士学位和博士学位;研究方向包括多机器人系统,新型移动机器人及人机协作等;在T-Ro、JFR、T-Mech、RA-L等机器人领域顶尖期刊和ICRA、IROS等机器人领域顶级会议上发表学术论文60余篇,出版英文专著2部,授权美国专利及国家发明专利60余项;以第一作者获IEEE/ASME Transactions on Mechatronics期刊年度最佳论文奖,以通信作者获IROS机器人机构设计最佳论文奖;相关研究成果被路透社、探索频道、日本NHK电视台、IEEE Spectrum、Wired等众多国际知名媒体广泛报导。


*关于AIRS研究院:

深圳市人工智能与机器人研究院(Shenzhen Institute of Artificial Intelligence and Robotics for Society,简称AIRS)是深圳市政府设立的十大基础研究机构之一,依托香港中文大学(深圳),联合多个世界顶级研究机构,以全新模式设立。

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