Cooperative Wireless-Based Obstacle/Object Mapping and See-Through Capabilities in Robotic Networks

In this paper, we develop a theoretical and experimental framework for the mapping of obstacles (including occluded ones), in a robotic cooperative network, based on a small number of wireless channel measurements. This would allow the robots to map an area before entering it. We consider three approaches based on coordinated space, random space, and frequency sampling, and show how the robots can exploit the sparse representation of the map in space, wavelet or spatial variations, in order to build it with minimal sensing. We then show the underlying tradeoffs of all the possible sampling, sparsity and reconstruction techniques. Our simulation and experimental results show the feasibility and performance of the proposed framework. More specifically, using our experimental robotic platform, we show preliminary results in successfully mapping a number of real obstacles and having see-through capabilities with real structures, despite the practical challenges presented by multipath fading.

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