We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in an indoor Real ENvironment via end-to-end reinforcement learning (RL). A suitable reward function is designed keeping in mind the cost and weight constraints for micro drone with minimum number of sensing modalities. Collection of small number of expert data and knowledge based data aggregation is integrated into the RL process to aid convergence. Experimentation is carried out on a Parrot AR drone in different indoor arenas and the results are compared with other baseline technologies. We demonstrate how the drone successfully avoids obstacles and navigates across different arenas. Video of the drone navigating using the proposed approach can be seen at https://youtu.be/yOTkTHUPNVY

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In this paper, we propose an efficient self-calibration mechanism, achieving localization as well as orientation, for wireless acoustic sensor network. Time difference of arrival (TDOA) based on wireless and acoustic signals, while direction of arrival (DOA) based on acoustic only, are used jointly to achieve acoustic sensor node calibration. Each sensor node is equipped with three microphones and a wireless transceiver. Sensor nodes are calibrated by using two beacon positions with respect to a reference node whose position is assumed to be known. Our approach requires a single moving beacon (MB) equipped with RF and acoustic signal sources. Once a node has been calibrated (in terms of location and orientation) it can be considered as a valid reference for the remaining uncalibrated nodes. We have developed hardware platform to validate the proposed calibration mechanism. Performance results show the effectiveness and usefulness of the proposed mechanism.

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