Harvard is on a robotic role with Origami self-folding robots and now the Radhika Nagpal, Mike Rubenstein, and Alex Cornejo awith a self-assembling swarm of 1,024 robots. These kilobots—where a kilo stands for 1,024—can form complex 2D shapes including a star, a wrench, and the letter “k.” Among the many challenges faced by the researchers is accounting for variations in the robots when forming complex shapes, which is the opposite challenge for animators who need to add variation to simulated swarms to make them look real.
Thier paper, “Programmable self-assembly in a thousand-robot swarm“, is the group’s second paper in Science this year:
Self-assembly enables nature to build complex forms, from multicellular organisms to complex animal structures such as flocks of birds, through the interaction of vast numbers of limited and unreliable individuals. Creating this ability in engineered systems poses challenges in the design of both algorithms and physical systems that can operate at such scales. We report a system that demonstrates programmable self-assembly of complex two-dimensional shapes with a thousand-robot swarm. This was enabled by creating autonomous robots designed to operate in large groups and to cooperate through local interactions and by developing a collective algorithm for shape formation that is highly robust to the variability and error characteristic of large-scale decentralized systems. This work advances the aim of creating artificial swarms with the capabilities of natural ones.
Their previous paper was “Designing Collective Behavior in a Termite-Inspired Robot Construction Team“:
Complex systems are characterized by many independent components whose low-level actions produce collective high-level results. Predicting high-level results given low-level rules is a key open challenge; the inverse problem, finding low-level rules that give specific outcomes, is in general still less understood. We present a multi-agent construction system inspired by mound-building termites, solving such an inverse problem. A user specifies a desired structure, and the system automatically generates low-level rules for independent climbing robots that guarantee production of that structure. Robots use only local sensing and coordinate their activity via the shared environment. We demonstrate the approach via a physical realization with three autonomous climbing robots limited to onboard sensing. This work advances the aim of engineering complex systems that achieve specific human-designed goals.
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