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1. Empirical Mode Decomposition (EMD) for Platform Motion Compensation in Remote Life Sensing Radar

   https://doi.org/10.1109/RWS53089.2022.9719920  

   Islam, Shekh M. ; Oba, Lupua ; Lubecke, Victor M. ( January 2022 , 2022 IEEE Radio and Wireless Symposium (RWS))

   Radar sensing of respiratory motion from unmanned aerial vehicles (UAVs) offers great promise for remote life sensing especially in post-disaster search and rescue applications. One major challenge for this technology is the management of motion artifacts from the moving UAV platform. Prior research has focused on using an adaptive filtering approach which requires installing a secondary radar module for capturing platform motion as a noise reference. This paper investigates the potential of the empirical mode decomposition (EMD) technique for the compensation of platform motion artifacts using only primary radar measurements. Experimental results demonstrated that the proposed EMD approach can extract the fundamental frequency of the breathing motion from the combined breathing and platform motion using only one radar, with an accuracy above 87%. 

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2. Identification of COVID-19 Type Respiratory Disorders Using Channel State Analysis of Wireless Communications Links

   https://doi.org/10.1109/EMBC46164.2021.9630016  

   Lubecke, Lana C. ; Ishmael, Khaldoon ; Zheng, Yao ; Boric-Lubecke, Olga ; Lubecke, Victor M. ( January 2021 , Annu Int Conf IEEE Eng Med Biol Soc)

   One deadly aspect of COVID-19 is that those infected can often be contagious before exhibiting overt symptoms. While methods such as temperature checks and sinus swabs have aided with early detection, the former does not always provide a reliable indicator of COVID-19, and the latter is invasive and requires significant human and material resources to administer. This paper presents a non-invasive COVID-19 early screening system implementable with commercial off-the-shelf wireless communications devices. The system leverages the Doppler radar principle to monitor respiratory-related chest motion and identifies breathing rates that indicate COVID-19 infection. A prototype was developed from software-defined radios (SDRs) designed for 5G NR wireless communications and system performance was evaluated using a robotic mover simulating human breathing, and using actual breathing, resulting in a consistent respiratory rate accuracy better than one breath per minute, exceeding that used in common medical practice.Clinical Relevance-This establishes the potential efficacy of wireless communications based radar for recognizing respiratory disorders such as COVID-19. 

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3. Motion Planning and Iterative Learning Control of a Modular Soft Robotic Snake

   https://doi.org/10.3389/frobt.2020.599242  

   Luo, Ming ; Wan, Zhenyu ; Sun, Yinan ; Skorina, Erik H. ; Tao, Weijia ; Chen, Fuchen ; Gopalka, Lakshay ; Yang, Hao ; Onal, Cagdas D. ( December 2020 , Frontiers in Robotics and AI)

   Snake robotics is an important research topic with a wide range of applications, including inspection in confined spaces, search-and-rescue, and disaster response. Snake robots are well-suited to these applications because of their versatility and adaptability to unstructured and constrained environments. In this paper, we introduce a soft pneumatic robotic snake that can imitate the capabilities of biological snakes, its soft body can provide flexibility and adaptability to the environment. This paper combines soft mobile robot modeling, proprioceptive feedback control, and motion planning to pave the way for functional soft robotic snake autonomy. We propose a pressure-operated soft robotic snake with a high degree of modularity that makes use of customized embedded flexible curvature sensing. On this platform, we introduce the use of iterative learning control using feedback from the on-board curvature sensors to enable the snake to automatically correct its gait for superior locomotion. We also present a motion planning and trajectory tracking algorithm using an adaptive bounding box, which allows for efficient motion planning that still takes into account the kinematic state of the soft robotic snake. We test this algorithm experimentally, and demonstrate its performance in obstacle avoidance scenarios. 

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4. Monitoring the earthquake response of full‐scale structures using UAV vision‐based techniques

   https://doi.org/10.1002/stc.2862  

   Wang, X ; Lo, E ; De Vivo, L ; Hutchinson, T ; Kuester, F ( January 2022 , Structural control health monitoring)

   Vision-based sensing, when utilized in conjunction with camera-equipped unmanned aerial vehicles (UAVs), has recently emerged as an effective sensing technique in a variety of civil engineering applications (e.g., construction monitoring, conditional assessment, and post-disaster reconnaissance). However, the use of these non-intrusive sensing techniques for extracting the dynamic response of structures has been restricted due to the perspective and scale distortions or image misalignments caused by the movement of the UAV and its on-board camera during flight operations. To overcome these limitations, a vision-based analysis methodology is proposed in the present study for extracting the dynamic response of structures using unmanned aerial vehicle (UAV) aerial videos. Importantly, geo-referenced targets were strategically placed on the structures and the background (stationary) region to enhance the robustness and accuracy related to image feature detection. Image processing and photogrammetric techniques are adopted in the analysis procedures first to recover the camera motion using the world-to-image correspondences of the background (stationary) targets and subsequently to extract the dynamic structural response by reprojecting the image feature of the (moving) targets attached to the structures to the world coordinates. The displacement tracking results are validated using the responses of two full-scale test structures measured by analog displacement sensors during a sequence of shake table tests. The high level of precision (less than 3 mm root-mean-square errors) of the vision-based structural displacement results demonstrates the effectiveness of the proposed UAV displacement tracking methodology. Additionally, the limitations and potential solutions associated with the proposed methodology for monitoring the dynamic responses of real structures are discussed. 

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5. AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training

   https://doi.org/10.3390/s20185223  

   Chen, Junjie ; Li, Shuai ; Liu, Donghai ; Li, Xueping ( September 2020 , Sensors)

   null (Ed.)

   Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots. 

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