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This paper addresses the problem of monitoring structures with potential emergent damage through adaptive sensing provided by teams of mobile robots. Advantages of mobile robot teams for structural health monitoring include: 1. Multiple views of a given structure, 2. Adaptive movements that focus attention in response to observed conditions,3. Heterogeneous sensing and movement, and 4. Federated health monitoring and prognosis assessment through networked sharing and processing of information. Towards this end three cases of the use of mobile robot teams will be presented: 1. Heterogeneous robot teams for home and small building maintenance – Identifying, diagnosing and mitigating damage to homes and small buildings is a vexing set of problems for the owners. As an aid small controlled bristlebots and quadruped robot dogs (QRDs) carry sensors throughout a small building, assess conditions, provide prognoses and networked links to repair options; 2. Culverts are primary components of stormwater and flood prevention infrastructure. Inspecting small culverts is difficult for humans and large culverts are accessible but dangerous due to issues of confined spaces. Low-cost mobile robots have emerged as a competitive inspection option for accessible culverts with straight or short runs that permit wireless telemetry. Longer culverts and those with bends, branches and drop inlets pose challenges to the telemetry. Teams of robots extend the range of inspection through multi-hop video and control telemetry; 3. Ground penetrating radar (GPR) is a method of sensing subsurface infrastructure conditions with high-frequency electromagnetic waves. Conventional GPRs operate in a suboptimal monostatic or bistatic mode, are tedious to operate and have limitations in sensing congested utility subsurface conditions. Coordinated multistatic ground penetrating radar operated with mobile robot teams alleviates some of these concerns and provide better subsurface assessments with automated methods that focus attention on subsurface features of interest. Results from laboratory and field tests of these robot teams, as well as organizing principles of control and automated information processing are presented. 

In this study, we demonstrate an application for 5G networks in mobile and remote GPR scanning situations to detect buried objects by experts while the operator is performing the scans. Using a GSSI SIR-30 system in conjunction with the RealSense camera for visual mapping of the surveyed area, subsurface GPR scans were created and transmitted for remote processing. Using mobile networks, the raw B-scan files were transmitted at a sufficient rate, a maximum of 0.034 ms mean latency, to enable near real-time edge processing. The performance of 5G networks in handling the data transmission for the GPR scans and edge computing was compared to the performance of 4G networks. In addition, long-range low-power devices, namely Wi-Fi HaLow and Wi-Fi hotspots, were compared as local alternatives to cellular networks. Augmented reality headset representation of the F-scans is proposed as a method of assisting the operator in using the edge-processed scans. These promising results bode well for the potential of remote processing of GPR data in augmented reality applications. 

Ground Penetrating Radar (GPR) is essential for subsurface exploration. Conventional GPR 3D imaging demands dense spatial sampling along regular grids, which is both time-consuming and impractical in complex environments. In this work, we propose a novel method that combines sparse recovery techniques with a placement matrix to merge arbitrarily and sparsely sampled measurements into a regular grid framework. By exploiting the inherent sparsity of subsurface targets and using the Dantzig Selector with cross-validation, our method reconstructs the target reflectivity vector from random spatial sampling. The recovered data is then processed via the Back-Projection Algorithm (BPA) to generate high-resolution 3D images. Simulations demonstrate that our approach not only improves imaging quality under reduced sampling conditions but also efficiently handles arbitrary scanning paths by mapping irregular measurements onto the desired grid.