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This paper presents a new, robust and reliable robot capable of carrying heavy equipment loads without sacrificing mobility that can improve the safety and detail of steel inspections in difficult access areas. In addition, the robot functions with an embedded NORTEC 600, eddy current sensor, and a GoPro camera that allows it to conduct nondestructive evaluation and collect high-resolution imagery data of steel structures. The data is processed into a heatmap for quick and easy interpretation by the user. In order to verify the robot’s designed capabilities, a set of mechanical analyses were performed to quantify the designed robot’s limits and failure mechanics. The application of our robot would increase the safety of an inspector by reducing the frequency they would need to hang underneath a bridge or travel along a narrow section. Demonstration of the robot deployments can be seen in this link: https://youtu.be/8d78d7CWXYk

The research of robots to assist people in inspecting the quality of steel bridges has attracted significant attention in recent years. However, the intricate structure of the steel bridge components poses a massive challenge for researchers to move the robot across the bridge to perform the tests. This paper presents a new development of a hybrid flying-climbing robotic system, which can move flexibly and quickly to different positions on the steel bridge. In addition to using high-resolution cameras for an overview, the design allows the robot to stick to steel surfaces and act as a mobile robot for more detailed inspection with our developed giant magneto-resistance (GMR) sensor array system. We conduct a mechanical analysis to show the climbing capability of the mobile part. Additionally, we develop a landing algorithm to allow the robot to land on a steel surface to perform in-depth inspection safely. The designed GMR sensor array has shown the capability of detecting steel cracks to support the in-depth inspection mode. We have tested and validated our developed robot on real bridges to ensure that the design works well and is stable.

The advanced robotic and automation (ARA) lab has developed and successfully implemented a design inspired by many of the various cutting edge steel inspection robots to date. The combination of these robots concepts into a unified design came with its own set of challenges since the parameters for these features sometimes conflicted. An extensive amount of design and analysis work was performed by the ARA lab in order to find a carefully tuned balance between the implemented features on the ARA robot and general functionality. Having successfully managed to implement this conglomerate of features represents a breakthrough to the industry of steel inspection robots as the ARA lab robot is capable of traversing most complex geometries found on steel structures while still maintaining its ability to efficiently travel along these structures; a feat yet to be done until now.

An industrial environment usually has a lot of waste that could cause harmful effects to both the products and the workers resulting in product defects, itchy eyes or chronic obstructive pulmonary disease, etc. While automatic cleaning robots could be used, the environment is often too large for one robot to clean alone in addition to the fact that it does not have adequate stored dirt capacity. We present a multi-robotic dirt cleaning algorithm for coordinating multiple iRobot-Creates as a team to efficiently clean an environment. Often, since some spaces in the environment are clean while others are dirty, our multi-robotic system possesses a path planning algorithm to allow the robot team to clean efficiently by increasing vacuum motor power on the area with higher dirt level. Overall, our multi-robotic system outperforms the single robot system in time efficiency while having almost the same total battery usage and cleaning efficiency result. The project source codes is available on our ARA lab's github: https://github.com/aralab-unr/multi-robot-cleaning.

Phytoplankton growth in the Indian Ocean is generally limited by macronutrients (nitrogen: N and phosphorus: P) in the north and by micronutrient (iron: Fe) in the south. Increasing atmospheric deposition of N and dissolved Fe (dFe) into the ocean due to human activities can thus lead to significant responses from both the northern and southern Indian Ocean ecosystems. Previous modeling studies investigated the impacts of anthropogenic nutrient deposition on the ocean, but their results are uncertain due to incomplete representations of the Fe cycling. This study uses a state‐of‐the‐art ocean ecosystem and Fe cycling model to evaluate the transient responses of ocean productivity and carbon uptake in the Indian Ocean, focusing on the centennial time scale. The model includes three major dFe sources and represents an internal Fe cycling modulated by scavenging, desorption, and complexation with multiple, spatially varying ligand classes. Sensitivity simulations show that after a century of anthropogenic deposition, ecosystem responses in the Indian Ocean are not uniform due to a competition between the phytoplankton community. In particular, the competition between diatom, coccolithophore, and picoplankton alters the balance between the organic and carbonate pumps in the Indian Ocean, increasing the carbon uptake along 50°S and the southeasternmore »tropics while decreasing it in the Arabian Sea. Our results reveal the important role of ecosystem dynamics in controlling the sensitivity of carbon fluxes in the Indian Ocean under the impact of anthropogenic nutrient deposition over a centennial timescale.

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Observations of dissolved iron (dFe) in the subtropical North Atlantic revealed remarkable features: While the near‐surface dFe concentration is low despite receiving high dust deposition, the subsurface dFe concentration is high. We test several hypotheses that might explain this feature in an ocean biogeochemistry model with a refined Fe cycling scheme. These hypotheses invoke a stronger lithogenic scavenging rate, rapid biological uptake, and a weaker binding between Fe and a ubiquitous, refractory ligand. While the standard model overestimates the surface dFe concentration, a 10‐time stronger biological uptake run causes a slight reduction in the model surface dFe. A tenfold decrease in the binding strength of the refractory ligand, suggested by recent observations, starts reproducing the observed dFe pattern, with a potential impact for the global nutrient distribution. An extreme value for the lithogenic scavenging rate can also match the model dFe with observations, but this process is still poorly constrained.

Critical‐sized defects remain a significant challenge in orthopaedics. 3D printed scaffolds are a promising treatment but are still limited due to inconsistent osseous integration. The goal of the study is to understand how changing the surface roughness of 3D printed titanium either by surface treatment or artificially printing rough topography impacts the mechanical and biological properties of 3D printed titanium. Titanium tensile samples and discs were printed via laser powder bed fusion. Roughness was manipulated by post‐processing printed samples or by directly printing rough features. Experimental groups in order of increasing surface roughness were Polished, Blasted, As Built, Sprouts, and Rough Sprouts. Tensile behavior of samples showed reduced strength with increasing surface roughness. MC3T3 pre‐osteoblasts were seeded on discs and analyzed for cellular proliferation, differentiation, and matrix deposition at 0, 2, and 4 weeks. Printing roughness diminished mechanical properties such as tensile strength and ductility without clear benefit to cell growth. Roughness features were printed on mesoscale, unlike samples in literature in which roughness on microscale demonstrated an increase in cell activity. The data suggest that printing artificial roughness on titanium scaffold is not an effective strategy to promote osseous integration.