July 17, 2020 | International, Aerospace
The Defense Innovation Unit has awarded Sierra Nevada Corporation a contract to build an orbital laboratory that would serve as a kind of unmanned space station, the company announced July 14.
The unmanned orbital outpost will be placed in low Earth orbit to be used for experiments and demonstrations. The autonomous, free flying vehicle will be able to host payloads and support space assembly and manufacturing, microgravity experimentation, logistics, training, testing and evaluations.
Under the contract, Sierra Nevada Corporation will repurpose their Shooting Star transport vehicle into a scalable, autonomous space station that can be used for experiments and demonstrations. The Shooting Star vehicle is a 16-foot attachment to the company's Dream Chaser space plane that was developed for NASA Commercial Resupply Services 2 missions. The vehicle was initially designed to provide extra payload storage and cargo disposal upon re-entry.
“We're excited by the multi-mission nature of Shooting Star,” Fatih Ozmen, SNC's chief executive, said in a statement. “It was originally developed for NASA resupply missions to the International Space Station, and since then we keep identifying new capabilities and solutions it offers to a wide variety of customers.”
The company's press release notes that while the initial orbital outpost will operate in LEO, future orbits could be placed in other orbits, including cislunar.
“The current Shooting Star is already designed with significant capabilities for an orbital outpost and by adding only a few components we are able to meet Department of Defense needs.” added Steve Lindsey, senior vice president of strategy for SNC's space systems. “We are proud to offer our transport vehicle to DoD as a free-flying destination for experimentation and testing, expanding beyond its current payload service capabilities for Dream Chaser cargo missions.”
According to the original DIU solicitation released June 2019, orbital outposts will need to be established in low Earth orbit within 24 months of the award. A DIU spokesperson told C4ISRNET at the time that the “the prototype will explore the military utility of exclusive DoD access to an unmanned orbital platform in order to perform experiments with no risk to human crew or other non-DOD payloads.”
December 16, 2021 | International, Aerospace, Naval, Land, C4ISR, Security
Feds will receive two weeks of paid leave to grieve the death of a child, under the fiscal 2022 National Defense Authorization Act.
May 22, 2020 | International, Naval
By Fernando Catta-Preta May 21, 2020 - Northrop Grumman has successfully manufactured and tested the first industry-built Very Lightweight Torpedo (VLWT) for the U.S. Navy. The prototype torpedo is based on the Pennsylvania State University Applied Research Laboratory's (PSU-ARL) design that was distributed to defense industrial manufacturers in 2016. Northrop Grumman, which independently funded the research and development, will offer the design-for-affordability improvements to this VLWT as Northrop Grumman's response for the Navy's Compact Rapid Attack Weapon program. Northrop Grumman‘s torpedo design and production legacy reaches back over 80 years to World War II through its Westinghouse acquisition. In 1943, Westinghouse won the Navy contract to reverse engineer a captured German electric torpedo and in 12 months began producing the MK18 electric torpedo, which turned the tide of the undersea warfare in the Pacific. Northrop Grumman has been at the forefront of torpedo design and production ever since, to include the current MK48 Common Broadband Advanced Sonar System (CBASS) heavyweight torpedo and MK50 Lightweight Torpedo. Today, Northrop Grumman is the only company in full rate production of MK54 and MK48 torpedo nose arrays and has delivered over 600 MK54 arrays and over 70 MK48 arrays to the U.S. Navy. Applying its engineering and manufacturing expertise, Northrop Grumman improved upon the VLWT baseline design to replace high-cost components and drive overall affordability, reproducibility and reliability. Those altered sections were built and tested using PSU-ARL's own test equipment for confidence. “The successful testing of the torpedo nose on the first try is a testament to Northrop Grumman's design-for-affordability approach, which will significantly reduce cost without sacrificing operational performance,” said David Portner, lead torpedo program manager, undersea systems, Northrop Grumman. Northrop Grumman assembled the prototype VLWT using a Stored Chemical Energy Propulsion System (SCEPS) manufactured by teammate Barber-Nichols, Inc., (BNI) of Denver, Colorado. “The nation needs advanced undersea warfare capabilities now more than ever," said Alan Lytle, vice president, undersea systems, Northrop Grumman. “We are ready to support fielding the VLWT which will increase subsea lethality and enable innovative concepts of operations for multiple warfighting platforms.” Northrop Grumman's manufacturing plan would span the country by building components in California, Utah, Minnesota, Colorado, West Virginia and Maryland. View source version on Northrop Grumman: https://news.northropgrumman.com/news/features/northrop-grumman-builds-very-lightweight-torpedo-for-us-navy
February 4, 2019 | International, C4ISR
A key ingredient in effective teams – whether athletic, business, or military – is trust, which is based in part on mutual understanding of team members' competence to fulfill assigned roles. When it comes to forming effective teams of humans and autonomous systems, humans need timely and accurate insights about their machine partners' skills, experience, and reliability to trust them in dynamic environments. At present, autonomous systems cannot provide real-time feedback when changing conditions such as weather or lighting cause their competency to fluctuate. The machines' lack of awareness of their own competence and their inability to communicate it to their human partners reduces trust and undermines team effectiveness. To help transform machines from simple tools to trusted partners, DARPA today announced the Competency-Aware Machine Learning (CAML) program. CAML aims to develop machine learning systems that continuously assess their own performance in time-critical, dynamic situations and communicate that information to human team-members in an easily understood format. “If the machine can say, ‘I do well in these conditions, but I don't have a lot of experience in those conditions,' that will allow a better human-machine teaming,” said Jiangying Zhou, a program manager in DARPA's Defense Sciences Office. “The partner then can make a more informed choice.” That dynamic would support a force-multiplying effect, since the human would know the capabilities of his or her machine partners at all times and could employ them efficiently and effectively. In contrast, Zhou noted the challenge with state-of-the-art autonomous systems, which cannot assess or communicate their competence in rapidly changing situations. “Under what conditions do you let the machine do its job? Under what conditions should you put supervision on it? Which assets, or combination of assets, are best for your task? These are the kinds of questions CAML systems would be able to answer,” she said. Using a simplified example involving autonomous car technology, Zhou described how valuable CAML technology could be to a rider trying to decide which of two self-driving vehicles would be better suited for driving at night in the rain. The first vehicle might communicate that at night in the rain it knows if it is seeing a person or an inanimate object with 90 percent accuracy, and that it has completed the task more than 1,000 times. The second vehicle might communicate that it can distinguish between a person and an inanimate object at night in the rain with 99 percent accuracy, but has performed the task less than 100 times. Equipped with this information, the rider could make an informed decision about which vehicle to use. DARPA has scheduled a pre-recorded webcast CAML Proposers Day for potential proposers on February 20, 2019. Details are available at: https://go.usa.gov/xE9aQ. The CAML program seeks expertise in machine learning, artificial intelligence, pattern recognition, knowledge representation and reasoning, autonomous system modeling, human-machine interface, and cognitive computing. To maximize the pool of innovative proposal concepts, DARPA strongly encourages participation by non-traditional proposers, including small businesses, academic and research institutions, and first-time Government contractors. DARPA anticipates posting a CAML Broad Agency Announcement solicitation to the Federal Business Opportunities website in mid-February 2019. https://www.darpa.mil/news-events/2019-01-31