Emergent behavior of particles on a lattice has been analyzed extensively in mathematics with possible analogies to physical phenomena such as clustering in colloidal systems. While there exists a rich pool of interesting results, most are yet to be explored physically due to the lack of experimental validation. Here we show how the individual moves of robotic agents are tightly mapped to a discrete algorithm and the emergent behaviors such as clustering are as predicted by the analysis of this algorithm. Taking advantage of the algorithmic perspective, we further designed robotic controls to manipulate the clustering behavior and show the potential for useful applications such as the transport of obstacles .
Active matter physics and swarm robotics have provided powerful tools for the study and control of ensembles driven by internal sources. At the macroscale, controlling swarms typically utilizes significant memory, processing power, and coordination unavailable at the microscale, e.g., for colloidal robots, which could be useful for fighting disease, fabricating intelligent textiles, and designing nanocomputers. To develop principles that that can leverage physics of interactions and thus can be utilized across scales, we take a two-pronged approach: a theoretical abstraction of URLanizing particle systems and an experimental robot system of active cohesive granular matter that intentionally lacks digital electronic computation and communication, using minimal (or no) sensing and control, to test theoretical predictions. We consider the problems of aggregation, dispersion, and collective transport. As predicted by the theory, as a parameter representing interparticle attraction increases, the robots transition from a dispersed phase to an aggregated one, forming a dense, compact collective. When aggregated, the collective can transport non-robot "impurities" in their environment, thus performing an emergent task driven by the physics underlying the transition. These results point to a fruitful interplay between algorithm design and active matter robophysics that can result in new nonequilibrium physics and principles for programming collectives without the need for complex algorithms or capabilities .
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Emergent behavior of particles on a lattice has been analyzed extensively in mathematics with possible analogies to physical phenomena such as clustering in colloidal systems. While there exists a rich pool of interesting results, most are yet to be explored physically due to the lack of experimental validation. Here we show how the individual moves of robotic agents are tightly mapped to a discrete algorithm and the emergent behaviors such as clustering are as predicted by the analysis of this algorithm. Taking advantage of the algorithmic perspective, we further designed robotic controls to manipulate the clustering behavior and show the potential for useful applications such as the transport of obstacles
Active matter physics and swarm robotics have provided powerful tools for the study and control of ensembles driven by internal sources. At the macroscale, controlling swarms typically utilizes significant memory, processing power, and coordination unavailable at the microscale, e.g., for colloidal robots, which could be useful for fighting disease, fabricating intelligent textiles, and designing nanocomputers. To develop principles that that can leverage physics of interactions and thus can be utilized across scales, we take a two-pronged approach: a theoretical abstraction of URLanizing particle systems and an experimental robot system of active cohesive granular matter that intentionally lacks digital electronic computation and communication, using minimal (or no) sensing and control, to test theoretical predictions. We consider the problems of aggregation, dispersion, and collective transport. As predicted by the theory, as a parameter representing interparticle attraction increases, the robots transition from a dispersed phase to an aggregated one, forming a dense, compact collective. When aggregated, the collective can transport non-robot "impurities" in their environment, thus performing an emergent task driven by the physics underlying the transition. These results point to a fruitful interplay between algorithm design and active matter robophysics that can result in new nonequilibrium physics and principles for programming collectives without the need for complex algorithms or capabilities