Search for: All records

Mobile robots can access regions and collect data in structural locations not easily reached by humans. This includes confined spaces, such as inside walls, and underground pipes; and remote spaces, such as the underside of bridge decks. Robot access provides the opportunity to sense in these difficult to access spaces with robot mounted sensors, i.e. cameras and lidars, and with the robot placing and servicing standalone sensors. Teams of robots, sensors and AR-equipped humans have the potential to provide rapid and more comprehensive structural assessments. This paper presents results of studies using small robots to explore and collect structural condition data from remote and confined spaces including in walls, culverts, and bridge deck undersides. The presentation also covers system and network architecture, methods for automating data processing with localized and edge-based processors, the use of augmented reality (AR) human interfaces and discusses key technical challenges and possible solutions. 

This paper presents research concerning the use of visual-inertial Simultaneous Localization And Mapping (SLAM) algorithms to aid in Continuous Wave (CW) radar target mapping. SLAM is an established field in which radar has been used to internally contribute to the localization algorithms. Instead, the application in this case is to use SLAM outputs to localize radar data and construct three-dimensional target maps which can be viewed live in augmented reality. These methods are transferable to other types of radar units and sensors, but this paper presents the research showing how the methods can be applied to calculate depth efficiently with CW radar through triangulation using a Boolean intersection algorithm. Localization of the radar target is achieved through quaternion algebra. Due to the compact nature of the SLAM and CW devices, the radar unit can be operated entirely handheld. Targets are scanned in a free-form manner where there is no need to have a gridded scanning layout. The main advantage to this method is eliminating many hours of usage training and expertise, thereby eliminating ambiguity in the location, size and depth of buried or hidden targets. Additionally, this method grants the user the additional power, penetration and sensitivity of CW radar without the lack of range finding. Applications include pipe and buried structure location, avalanche rescue, structural health monitoring and historical site research. 

Frequency modulated continuous wave (FMCW) radar allows for a wide range of research applications. One primary use of this technology which is explored in this paper is the ground penetrating radar. To achieve high sensing performance, wide-band spectral reconstruction and sophisticated image reconstruction algorithm have been developed to overcome hardware limitations. Applications and future work include Synthetic Aperture Radar (SAR) imaging, innovative GPR, and unmanned aerial vehicle (UAV) radar systems. 

The conventional ground penetrating radar (GPR) data analysis methods, which use piecemeal approaches in processing the GPR data formulated in variant formats such as A-Scan, B-Scan, and C-Scan, fail to provide a global view of underground objects on the fly to adapt the operations of GPR systems in the field. To bridge the gap, in this paper, we propose a novel GPR data analysis approach termed “ScanCloud” which is focused on the whole in situ GPR dataset rather than on individual A-Scans, B-Scans or C-Scans. We also study the integration of ScanCloud and a deep reinforcement learning method called deep deterministic policy gradient (DDPG) to adapt the operation of GPR system. The proposed method is evaluated using GPR modeling software called GprMax. Simulation results show the efficacy of ScanCloud and the adaptive GPR system enabled by the integration of ScanCluod and DDPG. 

Radio-frequency sensing and communication systems which use a waveform for more than one function offer the promise of improved spectral efficiency and streamlined hardware requirements. Control of orbital angular momentum (OAM) may be used to increase data-rates and improve radar sensitivity to certain chiral targets. This paper presents finite-difference time-domain simulations which model a gigahertz-frequency OAM radar capable of transmitting information via OAM-mode modulation. The unique chirality-detection capability of OAM radar is demonstrated, as well as simple information transmission. Simulation scope and radar specifications are designed with an eye toward developing a dual function ground penetrating radar (GPR) with OAM mode control. 

Ultra-wideband (UWB) ground penetrating radar (GPR) is an effective, widely used tool for detection and mapping of buried targets. However, traditional ground penetrating radar systems struggle to resolve and identify congested target configurations and irregularly shaped targets. This is a significant limitation for many municipalities who seek to use GPR to locate and image underground utility pipes. This research investigates the implementation of orbital angular momentum (OAM) control in an UWB GPR, with the goal of addressing these limitations. Control of OAM is a novel technique which leverages an additional degree of freedom offered by spatially structured helical waveforms. This paper examines several free-space and buried target configurations to determine the ability of helical OAM waveforms to improve detectability and distinguishability of buried objects including those with symmetric, asymmetric, and chiral geometries. Microwave OAM can be generated using a uniform circular array (UCA) of antennas with phase delays applied according to azimuth angle. Here, a four-channel network analyzer transceiver is connected to a UCA to enable UWB capability. The characteristic phase delays of OAM waveforms are implemented synthetically via signal processing. The viability demonstrated with the method opens design and analysis degrees of freedom for penetrating radar that may help with discerning challenging targets, such as buried landmines and wires. 

This paper presents research on the use of penetrating radar combined with 3-D computer vision for real-time augmented reality enabled target sensing. Small scale radar systems face the issue that positioning systems are inaccurate, non-portable or challenged by poor GPS signals. The addition of modern computer vision to current cutting-edge penetrating radar technology expands the common 2-D imaging plane to 6 degrees of freedom. Applying the fact that the radar scan itself is a vector with length equivalent to depth from the transmitting and receiving antennae, these technologies used in conjunction can generate an accurate 3-D model of the internal structure of any material for which radar can penetrate. The same computer vision device that localizes the radar data can also be used as the basis for an augmented reality system. Augmented reality radar technology has applications in threat detection (human through-wall, IED, landmine) as well as civil (wall and door structure, buried item detection). For this project, the goal is to create a data registration pipeline and display the radar scan data visually in a 3-D environment using localization from a computer vision tracking device. Processed radar traces are overlayed in real time to an augmented reality screen where the user can view the radar signal intensity to identify and classify targets.