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

Abstract

Abstract-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.

Capturing the human figure through a wall

“…Some of these systems have demonstrated the potential of using RF signals to recognize a handful of forward-backward gestures by matching them against prior training examples [Pu et al 2013]. Others have demonstrated using narrowband RF signals to map major obstacles and building interiors through walls [Depatla et al 2015;Mostofi 2012;Gonzalez-Ruiz et al 2014]. RF-Capture builds on this literature but extracts finer-grain information from RF signals.…”

Energy-Efficient Mobile Sensing in Distributed Multi-Agent Sensor Networks

“…Wang [15] applied CS to monitoring vehicle networks. Mostofi built maps in mobile networks [16] and robot networks [17] while the mobile sensors and robots were deployed outside the sensing areas. Huang [18] reconstructed a scalar field using MSNs and information fusion.…”

Occupancy distribution estimation for smart light delivery with perturbation-modulated light sensing

“…In computer vision, people are interested in estimating the visual hull of a 3D object using 2D images [28,29]. In robotics, an interesting problem is obstacle/object mapping -computing a spatial map to represent the obstacles or objects in the environment [30]. In wireless sensor networks, a related technique is Radio Tomographic Imaging (RTI), which uses the attenuation in received signal strength (RSS) caused by physical objects to create an image [31].…”