With just a random disturbance that energizes the cubes, they selectively self-assemble into a larger block. Photos courtesy of MIT CSAIL.
By Rachel Gordon | MIT CSAIL
While automated manufacturing is ubiquitous today, it was once a nascent field birthed by inventors such as Oliver Evans, who is credited with creating the first fully automated industrial process, in flour mill he built and gradually automated in the late 1700s. The processes for creating automated structures or machines are still very top-down, requiring humans, factories, or robots to do the assembling and making.
However, the way nature does assembly is ubiquitously bottom-up; animals and plants are self-assembled at a cellular level, relying on proteins to self-fold into target geometries that encode all the different functions that keep us ticking. For a more bio-inspired, bottom-up approach to assembly, then, human-architected materials need to do better on their own. Making them scalable, selective, and reprogrammable in a way that could mimic nature’s versatility means some teething problems, though.
Now, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have attempted to get over these growing pains with a new method: introducing magnetically reprogrammable materials that they coat different parts with — like robotic cubes — to let them self-assemble. Key to their process is a way to make these magnetic programs highly selective about what they connect with, enabling robust self-assembly into specific shapes and chosen configurations.
The soft magnetic material coating the researchers used, sourced from inexpensive refrigerator magnets, endows each of the cubes they built with a magnetic signature on each of its faces. The signatures ensure that each face is selectively attractive to only one other face from all the other cubes, in both translation and rotation. All of the cubes — which run for about 23 cents — can be magnetically programmed at a very fine resolution. Once they’re tossed into a water tank (they used eight cubes for a demo), with a totally random disturbance — you could even just shake them in a box — they’ll bump into each other. If they meet the wrong mate, they’ll drop off, but if they find their suitable mate, they’ll attach.
An analogy would be to think of a set of furniture parts that you need to assemble into a chair. Traditionally, you’d need a set of instructions to manually assemble parts into a chair (a top-down approach), but using the researchers’ method, these same parts, once programmed magnetically, would self-assemble into the chair using just a random disturbance that makes them collide. Without the signatures they generate, however, the chair would assemble with its legs in the wrong places.
“This work is a step forward in terms of the resolution, cost, and efficacy with which we can self-assemble particular structures,” says Martin Nisser, a PhD student in MIT’s Department of Electrical Engineering and Computer Science (EECS), an affiliate of CSAIL, and the lead author on a new paper about the system. “Prior work in self-assembly has typically required individual parts to be geometrically dissimilar, just like puzzle pieces, which requires individual fabrication of all the parts. Using magnetic programs, however, we can bulk-manufacture homogeneous parts and program them to acquire specific target structures, and importantly, reprogram them to acquire new shapes later on without having to refabricate the parts anew.”
Using the team’s magnetic plotting machine, one can stick a cube back in the plotter and reprogram it. Every time the plotter touches the material, it creates either a “north”- or “south”-oriented magnetic pixel on the cube’s soft magnetic coating, letting the cubes be repurposed to assemble new target shapes when required. Before plotting, a search algorithm checks each signature for mutual compatibility with all previously programmed signatures to ensure they are selective enough for successful self-assembly.
With self-assembly, you can go the passive or active route. With active assembly, robotic parts modulate their behavior online to locate, position, and bond to their neighbors, and each module needs to be embedded with hardware for the computation, sensing, and actuation required to self-assemble themselves. What’s more, a human or computer is needed in the loop to actively control the actuators embedded in each part to make it move. While active assembly has been successful in reconfiguring a variety of robotic systems, the cost and complexity of the electronics and actuators have been a significant barrier to scaling self-assembling hardware up in numbers and down in size.
With passive methods like these researchers’, there’s no need for embedded actuation and control.
Once programmed and set free under a random disturbance that gives them the energy to collide with one another, they’re on their own to shapeshift, without any guiding intelligence.
If you want a structure built from hundreds or thousands of parts, like a ladder or bridge, for example, you wouldn’t want to manufacture a million uniquely different parts, or to have to re-manufacture them when you need a second structure assembled.
The trick the team used toward this goal lies in the mathematical description of the magnetic signatures, which describes each signature as a 2D matrix of pixels. These matrices ensure that any magnetically programmed parts that shouldn’t connect will interact to produce just as many pixels in attraction as those in repulsion, letting them remain agnostic to all non-mating parts in both translation and rotation.
While the system is currently good enough to do self-assembly using a handful of cubes, the team wants to further develop the mathematical descriptions of the signatures. In particular, they want to leverage design heuristics that would enable assembly with very large numbers of cubes, while avoiding computationally expensive search algorithms.
“Self-assembly processes are ubiquitous in nature, leading to the incredibly complex and beautiful life we see all around us,” says Hod Lipson, the James and Sally Scapa Professor of Innovation at Columbia University, who was not involved in the paper. “But the underpinnings of self-assembly have baffled engineers: How do two proteins destined to join find each other in a soup of billions of other proteins? Lacking the answer, we have been able to self-assemble only relatively simple structures so far, and resort to top-down manufacturing for the rest. This paper goes a long way to answer this question, proposing a new way in which self-assembling building blocks can find each other. Hopefully, this will allow us to begin climbing the ladder of self-assembled complexity.”
Nisser wrote the paper alongside recent EECS graduates Yashaswini Makaram ’21 and Faraz Faruqi SM ’22, both of whom are former CSAIL affiliates; Ryo Suzuki, assistant professor of computer science at the University of Calgary; and MIT associate professor of EECS Stefanie Mueller, who is a CSAIL affiliate. They will present their research at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022).
The Internet of Things allows our smart gadgets in the home and wearable technologies like our smart watches to communicate and operate together. Image Credit: Ponchai nakumpa via Pixabay
Tiny internet-connected electronic devices are becoming ubiquitous. The so-called Internet of Things (IoT) allows our smart gadgets in the home and wearable technologies like our smart watches to communicate and operate together. IoT devices are increasingly used across all sorts of industries to drive interconnectivity and smart automation as part of the ‘fourth industrial revolution’.
The fourth industrial revolution builds on already widespread digital technology such as connected devices, artificial intelligence, robotics and 3D printing. It is expected to be a significant factor in revolutionising society, the economy and culture.
These small, autonomous, interconnected and often wireless devices are already playing a key role in our everyday lives by helping to make us more resource and energy-efficient, organised, safe, secure and healthy.
There is a key challenge, however – how to power these tiny devices. The obvious answer is “batteries”. But it is not quite that simple.
Many of these devices are too small to use a long-life battery and they are located in remote or hard-to-access locations – for instance in the middle of the ocean tracking a shipping container or at the top of a grain silo, monitoring levels of cereal. These types of locations make servicing some IoT devices extremely challenging and commercially and logistically infeasible.
Mike Hayes, head of ICT for energy efficiency at the Tyndall National Institute in Ireland, summarises the marketplace. ‘It’s projected that we are going to have one trillion sensors in the world by 2025,’ he said, ‘That is one thousand billion sensors.’
That number is not as crazy as it first seems, according to Hayes, who is the coordinator of the Horizon-funded EnABLES project (European Infrastructure Powering the Internet of Things).
If you think about the sensors in the technology someone might carry on their person or have in their car, home, office plus the sensors embedded in the infrastructure around them such as roads and railways, you can see where that number comes from, he explained.
“In the trillion IoT sensor world predicted for 2025, we are going to be throwing over 100 million batteries everyday into landfills unless we significantly extend battery life.”
– Mike Hayes, EnABLES
Landfill is not the only environmental concern. We also need to consider where all the material to make the batteries is going to come from. The EnABLES project is calling on the EU and industry leaders to think about battery life from the outset when designing IoT devices to ensure that batteries are not limiting the lifespan of devices.
‘We don’t need the device to last forever,’ said Hayes. ‘The trick is that you need to outlive the application that you’re serving. For example, if you want to monitor a piece of industrial equipment, you probably want it to last for five to 10 years. And in some cases, if you do a regular service every three years anyway, once the battery lasts more than three or four years that’s probably good enough.’
Although many devices have an operational life of more than 10 years, the battery life of wireless sensors is typically only one to two years.
The first step to longer battery life is increasing the energy supplied by batteries. Also, reducing the power consumption of devices will prolong the battery. But EnABLES is going even further.
The project brings together 11 leading European research institutes. With other stakeholders, EnABLES is working to develop innovative ways to harvest tiny ambient energies such as light, heat and vibration.
Harvesting such energies will further extend battery life. The goal is to create self-charging batteries that last longer or ultimately run autonomously.
mbient energy harvesters, such as a small vibrational harvester or indoor solar panel, that produce low amounts of power (in the milliwatt range) could significantly extend the battery life of many devices, according to Hayes. These include everyday items like watches, radio frequency identification (RFID) tags, hearing aids, carbon dioxide detectors, and temperature, light and humidity sensors.
EnABLES is also designing the other key technologies needed for tiny IoT devices. Not content with improving energy efficiency, the project is also trying to develop a framework and standardised and interoperable technologies for these devices.
One of the key challenges with autonomously powered IoT tools is power management. The energy source may be intermittent and at very low levels (microwatts), and different methods of harvesting supply different forms of power that require different techniques to convert to electricity.
Huw Davies, is chief executive officer of Trameto, a company which is developing power management for piezo electric applications. He points out that energy from photovoltaic devices tends to come in a steady trickle, while that from piezoelectric devices, which convert ambient energy from movements (vibrations) into electrical energy, generally comes in bursts.
‘You need a way of storing that energy locally in a store before it is delivered into a load, so you need to have ways of managing that,’ Davies said.
He is the project coordinator of the Horizon-funded HarvestAll project, which has developed an energy management system for ambient energy dubbed OptiJoule.
OptiJoule works with piezoelectric materials, photovoltaics and thermal electric generators. It can function with any of these sources on their own, or with multiple energy harvesting sources at the same time.
The goal is to enable autonomous sensors to be self-sustaining. In principle, it’s quite simple. ‘What we are talking about is ultra-low powered sensors taking some digital measurement,’ said Davies. ‘Temperature, humidity, pressure, whatever it is, with the data from that being delivered into the internet.’
The HarvestAll energy management integrated circuit device adjusts to match the different energy harvesters. It takes the different and intermittent energy created by these harvesters and stores it, for instance in a battery or capacitor, and then manages the delivery of a steady output of energy to the sensor.
Similarly to the EnABLES project, the idea is to create standardised technology that will enable the rapid development of long battery life/autonomous IoT devices in Europe and the world.
Davies said that the energy management circuit works completely autonomously and automatically. It is designed so that it can just be plugged into an energy harvester, or combination of harvesters, and a sensor. As a replacement for the battery it has a significant advantage, according to Davies, because ‘It will just work.’
Research in this article was funded by the EU.
This article was originally published in Horizon, the EU Research and Innovation magazine.