Biomechanical energy harvesting textiles based on nanogenerators that convert mechanical energy into electricity have broad application prospects in next-generation wearable electronic devices. However, the difficult-to-weave structure, limited flexibility and stretchability, small device size and poor weatherability of conventional nanogenerator-based devices have largely hindered their real-world application. Here, we report a highly stretchable triboelectric yarn that involves unique structure design based on intrinsically elastic silicone rubber tubes and extrinsically elastic built-in stainless steel yarns. By using a modified melt-spinning method, we realize scalable-manufacture of the self-powered yarn. A hundred-meter-length triboelectric yarn is demonstrated, but not limited to this size. The triboelectric yarn shows a large working strain (200%) and promising output. Moreover, it has superior performance in liquid, therefore showing all-weather durability. We also show that the development of this energy yarn facilitates the manufacturing of large-area self-powered textiles and provide an attractive direction for the study of amphibious wearable technologies.

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Abstract-A mobile robot we have developed is equipped with sensors to measure range to landmarks and can simultaneously localize itself as well as locate the landmarks. This modality is useful in those cases where environmental conditions preclude measurement of bearing (typically done optically) to landmarks. Here we extend the paradigm to consider the case where the landmarks (nodes of a sensor network) are able to measure range to each other. We show how the two capabilities are complimentary in being able to achieve a map of the landmarks and to provide localization for the moving robot. We present recent results with experiments on a robot operating in a randomly arranged network of nodes that can communicate via radio and range to each other using sonar. We find that incorporation of inter-node measurements helps reduce drift in positioning as well as leads to faster convergence of the map of the nodes. We find that addition of a mobile node makes the SLAM feasible in a sparsely connected network of nodes.