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Recent technological innovations in the areas of additive manufacturing and collaborative robotics have paved the way toward realizing the concept of on-demand, personalized production on the shop floor. Additive manufacturing process can provide the capability of printing highly customized parts based on various customer requirements. Autonomous, mobile systems provide the flexibility to move custom parts around the shop floor to various manufacturing operations, as needed by product requirements. In this work, we proposed a mobile additive manufacturing robot framework for merging an additive manufacturing process system with an autonomous mobile base. Two case studies showcase the potential benefits of the proposed mobile additive manufacturing framework. The first case study overviews the effect that a mobile system can have on a fused deposition modeling process. The second case study showcases how integrating a mobile additive manufacturing machine can improve the throughput of the manufacturing system. The major findings of this study are that the proposed mobile robotic AM has increased throughput by taking advantage of the travel time between operations/processing sites. It is particularly suited to perform intermittent operations (e.g., preparing feedstock) during the travel time of the robotic AM. One major implication of this study is its application in manufacturing structural components (e.g., concrete construction, and feedstock preparation during reconnaissance missions) in remote or extreme terrains with on-site or on-demand feedstocks.

Although Behavioral Cloning (BC) in theory suffers compounding errors, its scalability and simplicity still makes it an attractive imitation learning algorithm. In contrast, imitation approaches with adversarial training typically does not share the same problem, but necessitates interactions with the environment. Meanwhile, most imitation learning methods only utilises optimal datasets, which could be significantly more expensive to obtain than its suboptimal counterpart. A question that arises is, can we utilise the suboptimal dataset in a principled manner, which otherwise would have been idle? We propose a scalable model-based offline imitation learning algorithmic framework that leverages datasets collected by both suboptimal and optimal policies, and show that its worst case suboptimality becomes linear in the time horizon with respect to the expert samples. We empirically validate our theoretical results and show that the proposed method \textit{always} outperforms BC in the low data regime on simulated continuous control domains

Here, we report Landau regulation in Raman modes for geometric graphene structures including single-wall carbon nanotubes (SWNTs). First-principles calculations show that radial-tangential Raman modes (RTMs) (comprising radial and tangential Eigenvectors) appear in order, following radial breading mode (RBM) (~150 cm-1). RTMs reveal two degenerated or non-degenerated frequencies per node and extend upto G (~1590 cm-1). In Raman measurements for SWNTs, we demonstrate the existence of RTMs at ~180-1440 cm-1, which have been partially interpreted as RBM band for ~180-300 cm-1 and intermediate frequency mode for 600-1300 cm-1. We reinterpret the RBM band as a coalescent signature of RBM (~150-170 cm-1) and 1st node RTM (~170-190 cm-1) for geometric graphene structures of ~2.0-2.2 nm in diameter. The sequential appearance of RBM and RTM and the revelation of the double degeneracy are due to Landau regulation working on spatial variation of the radial and tangential Eigenvectors. Our findings provide a further way of understanding the nature of low dimensional materials utilizing Raman spectroscopy., Comment: 28 pages, 4 figures

Graphene has demonstrated great promise for technological use, yet control over material growth and understanding of how material imperfections affect the performance of devices are challenges that hamper the development of applications. In this work we reveal new insight into the connections between the performance of the graphene devices as environmental sensors and the microscopic details of the interactions at the sensing surface. Specifically, we monitor changes in the resistance of the chemical-vapour deposition grown graphene devices as exposed to different concentrations of ethanol. We perform thermal surface treatments after the devices are fabricated, use scanning probe microscopy to visualize their effects on the graphene sensing surface down to nanometer scale and correlate them with the measured performance of the device as an ethanol sensor. Our observations are compared to theoretical calculations of charge transfers between molecules and the graphene surface. We find that, although often overlooked, the surface cleanliness after device fabrication is responsible for the device performance and reliability. These results further our understanding of the mechanisms of sensing in graphene-based environmental sensors and pave the way to optimizing such devices, especially for their miniaturization, as with decreasing size of the active zone the potential role of contaminants will rise.

In this paper we present the design and fabrication of a portable device for environmental monitoring applications. This novel hand-held apparatus monitors the changes in the resistance of a sensing surface with a high accuracy and resolution and transmits the recorded data wirelessly to a cellphone. Such a design offers a solution for field testing of environmental sensors. The tested sensing surface in this study is based on an ultrathin material: graphene, which is placed on the surface of a Si/SiO2 wafer. This signal transducer and wireless communication system form together an ideal platform to harvest the sensitivity and selectivity of 2D materials for gas sensing applications., Comment: The paper will not be submitted for publication after all. It is not under peer review, and we request it be withdrawn for these reasons