{"id":688,"date":"2022-02-09T17:14:35","date_gmt":"2022-02-09T17:14:35","guid":{"rendered":"https:\/\/labs.ri.cmu.edu\/moonranger\/?p=688"},"modified":"2022-02-09T17:14:35","modified_gmt":"2022-02-09T17:14:35","slug":"between-a-moon-rock-and-a-hard-place","status":"publish","type":"post","link":"https:\/\/labs.ri.cmu.edu\/moonranger\/between-a-moon-rock-and-a-hard-place\/","title":{"rendered":"Between a Moon Rock and a Hard Place"},"content":{"rendered":"\t\t
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Imagine your favorite video game- perhaps it has nice greenery, trees, rocks and all of the small physical details that make it realistic. Simulation modeling for real-world application is not too different. Simulation modeling helps make a specific physical environment, in this case, the south pole of the Moon, available for testing on a computer. It helps predict performance of an entity, MoonRanger, and its activities, trekking the moon for ice. MoonRanger\u2019s simulation team is integral to the mission\u2019s success in helping test autonomy software and surface operations.<\/p>

Simulation is a very accessible testing method allowing the team to test different conditions and paths a number of times. Using the virtual tools, they can shift and alter the terrain to see how MoonRanger would fare on slightly varied missions. Since not a lot is known about the south pole in terms of rock distribution and topography, the team must utilize information from other missions to inform their work. The simulations help shape the rover\u2019s software, and ultimately its decision-making facilities or autonomy. The more information and potential outcomes the team provides, the more prepared the rover is to handle hazards such as, slopes, craters, rocks, and different optical properties, lunar lighting, shadowing and gravity.<\/p>

Since MoonRanger has four suspensionless wheels and is skid-steered, its mean-free path is dependent on rock size distributions and its affected mobility. Mean free path is the straight-line distance that a vehicle can attain 50 percent of the time without being impeded by a probabilistically-generated terrain. A vehicle with significant mobility, suspension, scale and ground clearance (i.e. a 4-wheel-drive farm tractor) will have a much longer mean free path in a given terrain than a suspensionless micro-rover like MoonRanger. Using something called the Monte-Carlo method, the team is able to generate many different trials, ultimately examining the rover\u2019s innate ability to traverse without any technological safeguards, such as perception.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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