大多数人认为海滨是城市的边缘。麻省理工学院的一组研究人员将其视为一个充满活力的, 乐高式建筑工地。
Most people think of the waterfront as the edge of the city. A team of MIT researchers sees it as a dynamic, Lego-like construction site.
他们的新系统,称为“FloatForm,”，是一群小型方形机器人船，它们在水面上组装成更大的结构,分解,并重新组装成新的,，所有这些都需要最少的人类指导。
Their new system, called “FloatForm,” is a swarm of small square robotic boats that assemble themselves into larger structures on the water, break apart, and reassemble into something new, all with minimal human direction.
每个机器人,大约有21厘米见方,餐盘大小，是一个独立的容器，有自己的推进器,传感器,和磁性闩锁。 , 它们共同暗示了未来，浮动基础设施将变得更具适应性: 紧急情况后的临时平台, 运河上的市场, 或一个在节日时出现并在人群回家时消失的舞台。
Each robot, about the size of a dinner plate at 21 centimeters square, is a self-contained vessel with its own thrusters, sensors, and magnetic latches. Together, they hint at a future in which floating infrastructure could become more adaptive: a temporary platform after an emergency, a market on a canal, or a stage that appears for a festival and dissolves when the crowd goes home.
“我们将其视为在水上形成基础设施,，使用模块化系统创建一个更大的系统,” 麻省理工学院 CSAIL 和 Senseable City Lab 的前研究员 Alejandro Gonzalez-Garcia, 说。 “如果’有紧急情况,你可以建造一座新的桥梁来缓解城市的交通。或者您可以创建浮动市场和浮动舞台。如果您想要一个更宜居的城市,，您也想使用水,。”
“We see it as forming infrastructure on the water, using a modular system to create one larger system,” says Alejandro Gonzalez-Garcia, a former researcher with MIT CSAIL and the Senseable City Lab. “If there的 an emergency, you could form a new bridge to alleviate traffic in the city. Or you could create floating markets and floating stages. If you want a more livable city, you want to use the water, too.”
今天在 Nature Communications, 上发表的开放获取工作, 来自麻省理工学院城市技术与规划实践教授、Senseable City Lab, 主任 Rus 和 Carlo Ratti, 的实验室，源自 Roboat, 他们与阿姆斯特丹高级大都会解决方案研究所的联合项目，该项目将全尺寸自主船舶放置在阿姆斯特丹的 运河上。这些运河曾经运送城市’的货物;今天,他们主要运送游客。
The open-access work, published today in Nature Communications, comes from the labs of Rus and Carlo Ratti, professor of practice of urban technologies and planning at MIT and director of the Senseable City Lab, and grows out of Roboat, their joint project with the Amsterdam Institute for Advanced Metropolitan Solutions that put full-size autonomous vessels on Amsterdam的 canals. Those canals once carried the city的 goods; today, they mostly carry tourists.
“我们探讨了运河是否可用于废物收集,或运输,，以将道路上的部分压力转移回水面,”说，Niklas Hagemann,是麻省理工学院建筑专业的研究生, CSAIL附属机构,，也是前Senseable City Lab研究员，他从项目的早期阶段就开始参与该项目。 “城市地区变得越来越密集,，因此您能否将公共空间扩展到目前未充分利用的水域’?”
“We explored whether the canals could be used for waste collection, or for transport, to offload some of the stress on the roads back onto the water,” says Niklas Hagemann, an MIT graduate student in architecture, CSAIL affiliate, and former Senseable City Lab researcher who has worked on the project since its early stages. “Urban areas are getting denser, so could you expand public space onto water that的 currently underutilized?”
FloatForm 将愿景缩小到桌面规模，以回答更难的问题: 如何让数十, 乃至数千, 的浮动机器人自行组织?
FloatForm shrinks that vision down to tabletop scale to answer a harder question: How do you get dozens, and eventually thousands, of floating robots to organize themselves?
研究小组在生物学中找到了答案。众所周知，火蚁通过将自己的身体连接成活筏,，在没有领导者精心设计组装的情况下，在洪水中幸存下来。每只蚂蚁都遵循简单的局部规则,，并且出现了弹性结构。
The team found its answer in biology. Fire ants famously survive floods by linking their bodies into living rafts, with no leader choreographing the assembly. Each ant follows simple local rules, and a resilient structure emerges.
“Each ant is an independent agent,” says Gonzalez-Garcia. “We wanted each robot to have its own capabilities, the same way ant colonies form a raft.”
大多数现有的自组装机器人系统, 在水上和其他地方, 都依赖于中央计算机来指示每一个动作。 That approach is vulnerable to single points of failure and scales poorly: The planning math balloons as robots are added, and the swarm must assemble sequentially, with most robots idling while they wait their turn. FloatForm 翻转天平。 A lightweight central planner steps in only sparingly, assigning each robot a final position to perfect the lattice, a level of geometric precision that purely distributed methods struggle to guarantee. Everything else, including navigating toward the target shape, avoiding collisions, and adapting to disturbances, runs on the robots themselves, which coordinate by exchanging positions with their immediate neighbors.整个蜂群同时移动。
Most existing self-assembling robot systems, on water and elsewhere, rely on a central computer dictating every move. That approach is vulnerable to single points of failure and scales poorly: The planning math balloons as robots are added, and the swarm must assemble sequentially, with most robots idling while they wait their turn. FloatForm flips the balance. A lightweight central planner steps in only sparingly, assigning each robot a final position to perfect the lattice, a level of geometric precision that purely distributed methods struggle to guarantee. Everything else, including navigating toward the target shape, avoiding collisions, and adapting to disturbances, runs on the robots themselves, which coordinate by exchanging positions with their immediate neighbors. The whole swarm moves at once.
这种并行性使得这部作品与众不同。 FloatForms 方法的规划复杂性仅取决于机器人的 本地邻居,，而不取决于群体的总大小。 “What we’re trying to do is to have minimal central intervention, and have them all move together at the same time,” says Gonzalez-Garcia.
That parallelism is what sets the work apart. The planning complexity of FloatForms approach depends only on a robot的 local neighbors, not the total size of the swarm. “What we’re trying to do is to have minimal central intervention, and have them all move together at the same time,” says Gonzalez-Garcia.
在麻省理工学院, 的实验中，八个机器人组成的车队反复从随机位置聚集成目标形状,，锁定在刚性结构, 中，按照命令分解,，重新组装成新的配置,，然后作为单个容器, 驶过水池，每次运行需要四到八分钟。在称为集体运输, 的最终模式, 中，规划者绘制整个结构的轨迹，每个机器人计算自己的贡献。 “每个机器人都成为执行器,” Gonzalez-Garcia 解释道。模拟显示该框架可以顺利扩展到 64 个集群。
In experiments at MIT, a fleet of eight robots repeatedly gathered from random positions into a target shape, latched into a rigid structure, broke apart on command, reassembled into a new configuration, and then drove across the pool as a single vessel, with each run taking four to eight minutes. In that final mode, called collective transport, a planner charts a trajectory for the whole structure and each robot computes its own contribution. “Every robot becomes an actuator,” Gonzalez-Garcia explains. Simulations showed the framework scaling smoothly to swarms of 64.
“这种很大程度上分散的方法的优点在于，随着群体的增长，计算不会’陷入困境,” Wang 说。 “无论您是使用八艘船还是80,，整个舰队都会同时协调和移动。因为总体组装时间原则上不会显着增加’t,，所以系统仍保持高度可扩展性。”
“The beauty of this largely decentralized approach is that the computation doesn’t get bogged down as the swarm grows,” says Wang. “Whether you are working with eight boats or 80, the entire fleet coordinates and moves simultaneously. Because the overall assembly time doesn’t significantly increase in principle, the system remains highly scalable.”
团结在一起也有的 的物质回报,。 “如果有波浪或水流,” Hagemann 说，通过像蚂蚁筏一样, 连接在一起，我们的船会变得更加稳定,”。
There的 a physical payoff to sticking together, too. “Our boats become more stable by joining together, like the ant raft, if you have waves or currents,” Hagemann says.
机器人通过完全隐藏在每个船体内部的闭锁机构进行连接。中心的单个伺服电机驱动受折纸启发的拉胀结构,，这是一种在所有方向上均匀收缩的几何形状,，将所有四个侧面的永磁体向内拉动以释放,，或将它们向外推动以跨越 10 到 15 厘米的间隙抓住邻居。磁铁以交替极性,排列，因此船可靠地卡入干净的方形格子中。
The robots connect through a latching mechanism hidden entirely inside each hull. A single servo motor at the center drives an origami-inspired auxetic structure, a geometry that contracts uniformly in all directions at once, pulling permanent magnets on all four sides inward to release, or pushing them outward to grab a neighbor across gaps of 10 to 15 centimeters. The magnets are arranged with alternating polarities, so the boats reliably click into clean square lattices.
优雅的部分是该机制不会’t做:消耗(much)功率。 3D 打印的齿轮箱可在电机关闭的情况下将闩锁保持在任一状态。 “它使用能量来锁定和解锁,，但在这些状态,之间，它不’t使用任何能量,” Hagemann说。对于可能将配置保存几个小时, 的基础设施来说，这一点很重要。 “因为机器人太小,你只能有这么大的电池,”添加了冈萨雷斯-加西亚。 “如果他们在锁定,时使用较少的能量，他们可以在计算,或实际移动时使用更多的能量。”
The elegant part is what the mechanism doesn’t do: consume (much) power. A 3D-printed gearbox holds the latch in either state with the motor switched off. “It uses energy to latch and de-latch, but in between those states, it doesn’t use any energy,” says Hagemann. For infrastructure that might hold a configuration for hours, that matters. “Because the robots are so small, you can only have a battery so big,” adds Gonzalez-Garcia. “If they use less energy on latching, they can use more on computation, or on actually moving.”
到达那里需要一些令人谦卑的工程。排列在 “X” 中的四个微型推进器为每个机器人提供全向运动,，包括就地转动,，但它们相对于机器人 微小的惯性, 具有很大的力，这使得早期的原型抽搐并且容易在低速下发生剧烈旋转。该团队添加了稳定翼以增加流体动力阻力，并调整控制器以在机器人之间保持稳健，, 在这个规模, 上永远不会完全相同。磁铁也带来了自己的问题: 它们的吸附力非常好，有时需要机器人将自己扭动才能解开。
Getting there took some humbling engineering. Four miniature thrusters arranged in an “X” give each robot omnidirectional motion, including turning in place, but they pack large forces relative to the robots tiny inertia, which made early prototypes twitchy and prone to aggressive spins at low speeds. The team added stabilizing fins to increase hydrodynamic drag and tuned the controllers to stay robust across robots that, at this scale, are never quite identical. The magnets posed their own problem: They held on so well that de-latching sometimes required the robots to twist themselves free.
从水箱到运河
From the tank to the canal
在 10 次试验, 中，系统在没有人工干预的情况下完成了任务，其中 90% 的情况下使用四个机器人，70% 的情况下使用八个机器人。当事情确实出错时,，架构显示出它的弹性: 短暂失去方向的机器人可以自行重新加入结构,，而不会使整个群体停止,，陷入编队僵局的机器人学会了摆脱自己并重试。
Across 10 trials, the system completed its missions without human intervention 90 percent of the time with four robots and 70 percent with eight. When things did go wrong, the architecture showed its resilience: A robot that briefly lost its bearings could rejoin the structure on its own, without bringing the whole swarm to a halt, and robots stuck in formation deadlocks learned to shake themselves free and retry.
从受控的室内水箱转移到真正的运河或港口需要的不仅仅是信心。 “Gonzalez-Garcia 说，的 船的大小与其可处理的扰动强度之间始终存在着关系,”。 “这些船非常小,，因此在非常扰动的水中,它们无法工作。”扩大规模将意味着可能通过机械联锁来加强闩锁,，就像使用的全尺寸Roboat,一样，并将实验室的超声波室内定位换成GPS或基于视觉的传感。很有帮助, 协调算法被设计为与传感器无关: 交换传感器, 保持逻辑。
Moving from a controlled indoor tank to a real canal or harbor will take more than confidence. “There的 always a relationship between the size of a boat and the magnitude of the disturbance it can handle,” says Gonzalez-Garcia. “These boats are very small, so in very disturbed water, they cannot work.” Scaling up will mean reinforcing the latches, potentially with mechanical interlocking like the full-size Roboat used, and trading the lab的 ultrasonic indoor positioning for GPS or vision-based sensing. Helpfully, the coordination algorithm was designed to be sensor-agnostic: swap the sensors, keep the logic.
该团队设想的应用远远超出城市运河,，从形成用于海上检查和维护的临时平台，到用于研究迁徙物种的自适应传感器网络，再到用于难以到达地区的应急响应的可重新配置的对接站。从临时施工平台到环境监测和科学考察，海上和远程操作,也具有潜力。
The team envisions applications well beyond city canals, from forming temporary platforms for offshore inspection and maintenance to adaptive sensor networks for studying migratory species to reconfigurable docking stations for emergency response in hard-to-reach areas. There is also potential for offshore and remote operations, from temporary construction platforms to environmental monitoring and scientific expeditions.
而且地理广阔。 “V威尼斯,荷兰,比利时,挪威的峡湾和湖泊,实际上任何拥有河流的城市都可以利用这一点,”冈萨雷斯-加西亚说。 “该项目使用的空间中水已经很重要,，但它也提出了一个问题:水还能在哪里发挥更多作用?”
And the geography is wide open. “Venice, the Netherlands, Belgium, the fjords and lakes of Norway, really any city with a river can take advantage of this,” says Gonzalez-Garcia. “The project uses spaces where water is already important, but it also raises the question: Where else can water be used for something more?”
“这是实现水上分布式集体行为的激动人心的一步,” 密歇根大学助理教授 Steven Ceron, 表示，他’t 没有参与这项研究。 “Asembly, 自重构, 和集体运动在干燥环境中已经足够困难,，但在水上以主要分布的方式实现这些行为是一个严重的额外挑战,，而该团队已经可靠地克服了它。通过将计算负担转移到机器人本身,，他们建立了一个更具弹性的系统，在不久的将来可以使像这样的机器人集体部署在开放水域环境中进行搜索操作,环境监测,和可重新配置的海洋基础设施。”
“This is an exciting step forward in realizing distributed collective behaviors on water,” says University of Michigan Assistant Professor Steven Ceron, who wasn’t involved in the research. “Assembly, self-reconfiguration, and collective motion are difficult enough in dry environments, but achieving these behaviors in a predominantly distributed fashion on water represents a serious additional challenge, and this team has credibly overcome it. By shifting the computational burden onto the robots themselves, they have built a more resilient system that in the near future could enable robot collectives like this to be deployed in open-water environments for search operations, environmental monitoring, and reconfigurable marine infrastructure.”
Gonzalez-Garcia, Hagemann, 和 Wang 与资深作者 Ratti, 共同撰写了这篇论文，Ratti, 也是米兰理工大学, 和俄罗斯大学的教授。 Gonzalez-Garcia 还隶属于鲁汶大学的 MECO 研究团队。该研究得到了阿姆斯特丹高级都市解决方案研究所, 的资助以及威斯康星大学麦迪逊分校的额外支持。该团队感谢麻省理工学院 Sea Grant 和 Michael Triantafyllou 教授提供了测试水箱。
Gonzalez-Garcia, Hagemann, and Wang wrote the paper with senior authors Ratti, who is also a professor at Politecnico di Milano, and Rus. Gonzalez-Garcia is additionally affiliated with the MECO Research Team at KU Leuven. The research was supported by a grant from the Amsterdam Institute for Advanced Metropolitan Solutions, with additional support from the University of Wisconsin at Madison. The team thanks MIT Sea Grant and Professor Michael Triantafyllou for providing the test tank.