As in previous experiments, the new system involves large usable structures built from a series of tiny identical subunits called voxels (the volumetric equivalent of a 2-D pixel). But whereas previous voxels were purely mechanical structural pieces, the team has now developed complex voxels that can carry both energy and data from one unit to another. This could enable structures to be built that can not only bear loads but also perform jobs, such as lifting, moving and manipulating materials, including the voxels themselves.
“When we build these structures, you have to integrate intelligence,” Gershenfeld says. While earlier versions of the assembler robots were connected via cable bundles to their power source and control systems, “what emerged was the idea of structural electronics: to create voxels that transmit energy, data and force” . Looking at the new system in operation, he points out: “There are no cables. There is only the structure.
The bots themselves consist of a string of several voxels joined end-to-end. These can grab hold of another voxel using the attachment points on one end, then move like a worm to the desired location, where the voxel can be attached to the growing structure and released there.
Gershenfeld explains that while the previous system demonstrated by members of his group could in principle build arbitrarily large structures, as the size of those structures reached a certain point in relation to the size of the assembling robot, the process would become increasingly inefficient due to of the ever-longer paths that each bot would have to travel to get each piece to its destination. At that point, with the new system, bots could decide it was time to build a bigger version of themselves that could reach greater distances and reduce travel time. An even larger structure might require yet another such step, with new, larger robots creating even larger ones, while parts of a structure that include lots of fine detail might require multiple smaller robots.
As these robotic devices work to assemble something, Abdel-Rahman says, they are faced with choices every step along the way: “It could build a structure, or it could build another robot of the same size, or it could build a bigger robot. ” Part of the work the researchers have focused on is creating the algorithms for such decision making.
“For example, if you want to build a cone or a hemisphere,” he says, “how do you start planning the path and how do you divide this shape” into different areas for different robots to work on? The software they developed allows someone to input a shape and get an output showing where to place the first block, and each one after, based on the distances they need to travel.
There are thousands of articles published on path planning for robots, Gershenfeld says. “But the next step, where the robot has to make the decision to build another robot or a different type of robot, is new. There is really nothing before this.
While the experimental system can do the assembly and includes the power and data connections, in current versions the connectors between the tiny subunits aren’t strong enough to handle the necessary loads. The team, including graduate student Miana Smith, is now focusing on developing stronger connectors. “These robots can walk and they can place parts,” says Gershenfeld, “but we’re almost — but not quite — to the point where one of these robots does another and walks away. And that depends on how things are fine-tuned.” , like the strength of the actuators and the strength of the joints. … But it’s far enough forward that these are the parts that are going to carry us.
Ultimately, such systems could be used to build a wide variety of large, high-value structures. For example, currently the way airplanes are built involves huge factories with portals much larger than the components they build, and so “when you make a jumbo jet, you need jumbo jets to carry the jumbo jet parts to make it”, Gershenfeld says. With a system like this made up of tiny components put together by tiny robots, “the final assembly of the aircraft is the only assembly.”
Similarly, in the production of a new car, “you can spend a year tooling up” before the first car is actually built, he says. The new system would bypass the whole process. Such potential efficiencies are why Gershenfeld and his students have worked closely with automakers, airlines and NASA. But the relatively low-tech construction industry could also benefit.
While there has been growing interest in 3D printed houses, today those require printing machines as large or larger than the house being built. Again, the potential for such structures to be assembled by swarms of tiny robots instead could provide benefits. And the Defense Advanced Research Projects Agency is also interested in working on the possibility of building structures for coastal protection against erosion and sea level rise.
Aaron Becker, an associate professor of electrical and computer engineering at the University of Houston who was not associated with this research, calls this paper “a home run — [offering] an innovative hardware system, a new way of thinking about scaling a swarm and rigorous algorithms.
Becker adds, “This paper looks at a critical area of reconfigurable systems: how to rapidly scale up a robotic workforce and use it to efficiently assemble materials into a desired structure. … This is the first work I’ve seen that approaches the problem from a radically new perspective — using a raw set of robot parts to build a suite of robots whose dimensions are optimized to build the desired structure (and other robots) at the same possible speed.”
The research team also included MIT-CBA student Benjamin Jenett and Christopher Cameron, who now works at the US Army Research Laboratory. The work was supported by NASA, the US Army Research Laboratory and funding from the CBA consortia.