One of the primary impediments to building ensembles of modular robots is the complexity and number of mechanical mechanisms used to construct the individual modules. As part of the Claytronics project - which aims to build very large ensembles of modular robots - we investigate how to simplify each module by eliminating moving parts and reducing the number of mechanical mechanisms on each robot by using force-at-a-distance actuators. Additionally, we are also investigating the feasibility of using these unary actuators to improve docking performance, implement intermodule adhesion, power transfer, communication, and sensing. In this paper we describe our most recent results in the magnetic domain, including our first design sufficiently robust to operate reliably in groups greater than two modules. Our work should be seen as an extension of systems such as Fracta [9], and a contrasting line of inquiry to several other researchers' prior efforts that have used magnetic latching to attach modules to one another but relied upon a powered hinge [10] or telescoping mechanism [12] within each module to facilitate self-reconfiguration.

Lab-on-a-chip (LoC) systems can be functionally decomposed into their basic operating devices. Common devices are mixers, reactors, injectors, and separators. In this paper, the injector device is modeled using artificial neural networks (NNs) trained with finite element simulations of the underlying mass transport partial differential equations (PDEs). This technique is used to map the injector behavior into a set of analytical performance functions parameterized by the system's physical variables. The injector examples shown are the cross, the double-tee, and the gated-cross. The results are four orders of magnitude faster than numerical simulation and accurate with mean square errors (MSEs) on the order of$10^-4$. The resulting NN training data compare favorably with experimental data from a gated-cross injector found in the literature.

We propose a demonstration of extremely scalable modular robotics algorithms developed as part of the Claytronics Project (http://www-2.cs.cmu.edu/~claytronics/), as well as a demonstration of proof-of-concept prototypes. Our effort envisions multi-million-module robot ensembles able to morph into three-dimensional scenes, eventually with sufficient fidelity so as to convince a human observer the scenes are real. Although this work is potentially revolutionary in the sense that it holds out the possibility of radically altering the relationship between computation, humans, and the physical world, many of the research questions involved are similar in flavor to more mainstream systems research, albeit larger in scale. For instance, as in sensor networks, each robot will incorporate sensing, computation, and communications components. However, unlike most sensor networks each robot will also include mechanisms for actuation and motion. Many of the key challenges in this project involve coordination and communication of sensing and actuation across such large ensembles of independent units.