Tag Archives: University of Maryland

TMC’s latest offshore polymetallic nodule research campaign sets sail

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The Metals Company, an explorer of “lower-impact battery metals from seafloor polymetallic nodules”, has mobilised its latest offshore research campaign, Environmental Expedition 5C, which continues its investigation of the pelagic zone in its NORI-D licence area of the Clarion Clipperton Zone in the Pacific Ocean.

The company’s fourth environmental campaign this year, Expedition 5C is the latest work package in The Metals Company’s multi-year deep-sea research program intended to establish a rigorous environmental baseline and characterise the potential impacts of its proposed nodule collection operations to source critical battery metals from deep-sea polymetallic nodules, the company says.

Setting sail this week aboard the exploration vessel the Maersk Launcher, researchers from the University of Hawaiʻi at Mānoa, University of Maryland, Texas A&M and the Japan Agency for Marine-Earth Science and Technology will conduct numerous studies over the six-week expedition to further characterise the biological species and food web structure from the ocean surface to the benthic boundary layer, just above the abyssal seafloor at depths of up to 4,500 m. In addition, researchers will continue examining the chemistry, trace metal and nutrient profiles found throughout the water column.

At the researchers’ disposal will be specialised equipment including hydrographic rosettes to collect water samples, a Saildrone autonomous vehicle and MOCNESS nets, which will be used to sample micronekton and zooplankton communities throughout the water column.

“The collection and analysis of this baseline data is a critical component of the Environmental, Social and Impact Assessment (ESIA) required to establish the state of the ecosystem as it exists prior to the commencement of nodule collection and to assist in predicting the potential effects on the surrounding environment,” Dr Michael Clarke, TMC’s Environmental Program Manager, says. “The team of researchers on this expedition are at the top of their fields and the research they produce will contribute greatly to advance society’s knowledge of the Clarion Clipperton Zone.”

TMC’s NORI-D nodule project is the first in the company’s project development pipeline. In January, The Metals Company published an upward revision to the nodule resource reported within the NORI-D area held by its subsidiary, Nauru Ocean Resources Inc (NORI), improving resource confidence from inferred to indicated status. Resource tonnage increased by 7% over the reported area from 320 Mt inferred to 341 Mt indicated. The positive conversion rates arising from infill sampling grid with quality box core sample data are high compared to the typical outcomes from infill sampling of terrestrial mineral deposits.

Baidu Research Robotics and Auto-Driving Lab present autonomous excavator concept

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Researchers from Baidu Research Robotics and Auto-Driving Lab (RAL) and the University of Maryland, College Park (UMD) say they have developed a real-time mapping approach for autonomous navigation of excavators on complex terrains, named Terrain Traversability Mapping (TTM), that is being showcased at MINExpo 2021 this week.

Using TTM, an autonomous excavator can navigate through unstructured outdoor environments consisting of deep pits, steep hills, rock piles and other complex terrain features, according to the companies involved. This is the first complex terrain processing approach developed for heavy-duty excavation machines, they say.

To enable autonomous excavators to handle complex terrains, the researchers developed an efficient learning-based geometric method to extract terrain features from RGB images and 3D point clouds and incorporate them into a global map for planning and navigation.

The method uses physical characteristics of the excavator, including maximum climbing degree and machine specifications, to determine the traversable area, adapt to changing environments and update the terrain information in real time.

In addition, these researchers have prepared a “novel” autonomous excavator terrain dataset, which consists of RGB images and LiDAR point clouds from construction sites with seven different categories based on navigability. This integrates the mapping approach with planning and control modules to continuously improve the autonomous excavator navigation system, they say.

“Experiments showed that while using TTM, an excavator can navigate through unstructured environments with a much higher success rate compared to existing planning schemes,” the companies said.

The new TTM technique comes on the heels of another innovation Baidu RAL and UMD co-developed this June – an autonomous excavator system (AES) that can perform material loading tasks for 24 hours without any human intervention, while offering performance nearly equivalent to that of an experienced human operator.