Mobile intelligent networks can play a key role in many different areas from emergency response, surveillance and security, and battlefield operations to smart homes and factories and environmental monitoring. Accurate mapping of the obstacles/objects in the environment is key to the robust operation of unmanned autonomous networks as it is an integral part of navigation and path planning. In the robotics community, the problem of mapping has been widely explored. However, in the existing mapping approaches, only areas that can be directly sensed by the sensors are mapped. In several scenarios, it may be necessary to have see-through capabilities and map occluded objects without direct sensing. For instance, the robots may need to build an understanding of the objects inside a room before entering it. Having see-through capabilities can also reduce the overall mapping time and energy in any networked robotic operation. In this dissertation, we consider a mobile robotic network that is tasked with building a map of the objects/obstacles in an environment including the occluded ones. Since we are interested in see-through capabilities, in our framework the robots cooperate to build the map based on a small number of wireless channel measurements. This allows the robots to efficiently map occluded areas of the workspace. By using the recent results in the area of compressive sensing, we exploit the sparse representation of the map in space, wavelet or spatial variations, in order to build it with minimal sensing. We discuss three mapping strategies based on frequency sampling, coordinated space and random space measurements and show the underlying tradeoffs of the possible sampling, sparsity and reconstruction techniques. For instance, we shed light on the optimum number of angular motion directions of the robots, as well as the choice of the angles, to distribute a given number of wireless measurements. We establish that the total number of available channel measurements should be distributed over a small number of angles, that is bigger than or equal to the number of jump (discontinuity) angles of the structure, with a preference given to the angles of jumps. We then propose an integration of our wireless-based mapping framework with existing mapping techniques in order to map more complicated structures. More specifically, we propose an integrated framework where laser measurements are used to map the visible parts of the environment (the parts that can be sensed directly by the laser scanners) using occupancy grid mapping approaches. The parts that can not properly be mapped are then identified and mapped based on wireless channel measurements. We show how to integrate occupancy grid mapping with two reconstruction methods based on wireless measurements and compressive sensing: Bayesian compressive sensing (BCS) and total variation (TV) minimization. We compare the performance of these two integrated approaches and shed light on the underlying tradeoffs. Finally, we propose an adaptive path planning strategy that utilizes the current estimate of uncertainty to collect wireless measurements that are more informative for obstacle mapping. Overall, our integrated framework enables mapping occluded structures that can not be mapped with laser scanner data alone or a small number of wireless measurements. Most importantly, we show how to design an experimental robotic platform in order to implement our approach. We then show the performance of our framework in efficiently mapping a number of real obstacles including blocked ones. Our experimental results confirm the feasibility of the proposed framework for mapping structures that include occluded parts.
Wireless sensor networks., Multisensor data fusion., Mobile agents (Computer software)., Context-aware computing., Data compression (Telecommunication)
Level of Degree
Electrical and Computer Engineering
First Committee Member (Chair)
Second Committee Member
Third Committee Member
Gonzalez Ruiz, Alejandro. "Compressive cooperative obstacle mapping with see-through capabilities in mobile networks." (2013). https://digitalrepository.unm.edu/ece_etds/102