3 juin 2021 | International, Terrestre
Rheinmetall delivers first batch of Boxer scouting vehicles to Australia
Twenty-five vehicles done, 106 to go for the combat reconnaissance version of Australia's future Boxer fleet.
7 février 2019 | International, Sécurité, Autre défense
DARPA: Intelligent Healing for Complex Wounds
Blast injuries, burns, and other wounds experienced by warfighters often catastrophically damage their bones, skin, and nerves, resulting in months to years of recovery for the most severe injuries and often returning imperfect results. This long and limited healing process means prolonged pain and hardship for the patient, and a drop in readiness for the military. However, DARPA believes that recent advances in biosensors, actuators, and artificial intelligence could be extended and integrated to dramatically improve tissue regeneration. To achieve this, the new Bioelectronics for Tissue Regeneration (BETR) program asks researchers to develop bioelectronics that closely track the progress of the wound and then stimulate healing processes in real time to optimize tissue repair and regeneration.
Paul Sheehan, the BETR program manager, described his vision for the technology as “not just personalized medicine, but dynamic, adaptive, and precise human therapies” that adjust to the wound state moment by moment to provide greater resilience to wounded warfighters.
“Wounds are living environments and the conditions change quickly as cells and tissues communicate and attempt to repair,” Sheehan said. “An ideal treatment would sense, process, and respond to these changes in the wound state and intervene to correct and speed recovery. For example, we anticipate interventions that modulate immune response, recruit necessary cell types to the wound, or direct how stem cells differentiate to expedite healing.”
The envisioned BETR technology would represent a sharp break from traditional wound treatments, and even from other emerging technologies to facilitate recovery, most of which are passive in nature.
Under current medical practice, physicians provide the conditions and time for the body to either heal itself when tissues have regenerative capacity or to accept and heal around direct transplants. Most people are familiar with interventions that include casts to stabilize broken bones or transplants of healthy ligaments or organs from donors to replace tissues that do not regenerate.
Passive approaches often result in slow healing, incomplete healing with scarring, or, in some unfortunate cases, no healing at all. Blast injuries in particular seem to scramble the healing processes; 23 percent of them will not fully close. Moreover, research shows that in nearly two thirds of military trauma cases — a rate far higher than with civilian trauma injuries — these patients suffer abnormal bone growth in their soft tissue due to a condition known as heterotopic ossification, a painful experience that can greatly limit future mobility.
Although recent experimental treatments offer some hope for expedited recovery, many of these new approaches remain static in nature. For instance, some “smart” bandages emit a continuous weak electric field or locally deliver drugs. Alternatively, hydrogel scaffolds laced with a drug can recruit stem cells, while decellularized tissue re-seeded with donor cells from the patient help avoid rejection by the host's immune system. These newer approaches may indeed encourage growth of otherwise non-regenerative tissue, but because they do not adapt to the changing state of a wound, their impact is limited.
“To understand the importance of adaptive treatments that respond to the wound state, consider the case of antibiotic ointments,” Sheehan explained. “People use antibiotics to treat simple cuts, and they help if the wound is infected. However, completely wiping out the natural microbiota can impair healing. Thus, without feedback, antibiotics can become counterproductive.”
Recent technologies have begun to close the loop between sensing and intervention, looking for signs of infection such as changes in pH level or temperature to trigger treatment. To date, however, these systems have been limited to monitoring changes induced by bacteria. For BETR, DARPA intends to use any available signal, be it optical, biochemical, bioelectronic, or mechanical, to directly monitor the body's physiological processes and then to stimulate them to bring them under control, thereby speeding healing or avoiding scarring or other forms of abnormal healing.
By the conclusion of the four-year BETR program, DARPA expects researchers to demonstrate a closed-loop, adaptive system that includes sensors to assess wound state and track the body's complex responses to interventions; biological actuators that transmit appropriate biochemical and biophysical signals precisely over space and time to influence healing; and adaptive learning approaches to process data, build models, and determine interventions. To succeed, the BETR system must yield faster healing of recalcitrant wounds, superior scar-free healing, and/or the ability to redirect abnormally healing wounds toward a more salutary pathway.
DARPA anticipates that successful teams will include expertise in bioelectronics, artificial intelligence, biosensors, tissue engineering, and cellular regeneration. Further, DARPA encourages proposals that address healing following osseointegration surgery, which is often necessary to support the use of advanced prosthetics by wounded warfighters.
DARPA will host a Proposers Day on March 1, 2019 in Arlington, Virginia, to provide more information to researchers interested in submitting a proposal for funding. Additional information is available at https://go.usa.gov/xENCQ. A forthcoming Broad Agency Announcement, to be posted to the Federal Business Opportunities website, will include full details of the program.
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8 septembre 2020 | International, Aérospatial
GA-ASI Demonstrates AI Driven Targeting Computer with AFRL’s Agile Condor Pod
General Atomics Aeronautical Systems, Inc., with the support of SRC Inc., successfully integrated and flew the Air Force Research Laboratory's (AFRL) Agile Condor Pod on an MQ-9 Remotely Piloted Aircraft at GA-ASI's Flight Test and Training Center in Grand Forks, North Dakota The Agile Condor Pod provides on-board high-speed computer processing coupled with machine learning algorithms to detect, correlate, identify, and track targets of interest. With this capability, the MQ-9 is able to identify objects autonomously utilizing its on-board Electro-optical/Infrared (EO/IR) sensor and GA-ASI's Lynx Synthetic Aperture Radar (SAR). Defense contractor SRC, Inc. developed the Agile Condor system for the Air Force Research Laboratory (AFRL), delivering the first pod in 2016. It's not clear whether the Air Force conducted any flight testing of the system on other platforms before hiring General Atomics to integrate it onto the Reaper in 2019. The service had previously said that it expected to take the initial pod aloft in some fashion before the end of 2016. High-powered computing at the edge enables autonomous target detection, identification and nomination at extended ranges and on-board processing reduces communication bandwidth requirements to share target information with other platforms. This is an important step towards greater automation, autonomous target detection, and rapid decision-making. GA-ASI will continue to work with AFRL to refine the capability and foster its transition to operational constructs that will improve warfighters' ability to operate in contested or denied environments. “Sensors have rapidly increased in fidelity, and are now able to collect vast quantities of data, which must be analyzed promptly to provide mission critical information,” an SRC white paper on Agile Condor from 2018 explains. “Stored data [physically on a drone] ... creates an unacceptable latency between data collection and analysis, as operators must wait for the RPA [remotely piloted aircraft] to return to base to review time sensitive data.” “In-mission data transfers, by contrast, can provide data more quickly, but this method requires more power and available bandwidth to send data,” the white paper continues. “Bandwidth limits result in slower downloads of large data files, a clogged communications link and increased latency that could allow potential changes in intel between data collection and analysis. The quantities of data being collected are also so vast, that analysts are unable to fully review the data received to ensure actionable information is obtained.” This is all particularly true for drones equipped with wide-area persistent surveillance systems, such as the Air Force's Gorgon Stare system, which you can read about in more detail here, that grab immense amounts of imagery that can be overwhelming for sensor operators and intelligence analysts to scour through. Agile Condor is designed to parse through the sensor data a drone collects first, spotting and classifying objects of interest and then highlighting them for operators back at a control center or personnel receiving information at other remote locations for further analysis. Agile Condor would simply discard “empty” imagery and other data that shows nothing it deems useful, not even bothering to forward that on. “This selective ‘detect and notify' process frees up bandwidth and increases transfer speeds, while reducing latency between data collection and analysis,” SRC's 2018 white paper says. “Real time pre-processing of data with the Agile Condor system also ensures that all data collected is reviewed quickly, increasing the speed and effectiveness with which operators are notified of actionable information.” At least at present, the general idea is still to have a human operator in the ‘kill chain' making decisions about how to act on such information, including whether or not to initiate a lethal strike. The Air Force has been emphatic about ensuring that there will be an actual person in the loop at all times, no matter how autonomous a drone or other unmanned vehicle may be in the future. An Air Force Research Laboratory briefing slide showing a concept of operations for how a drone might use Agile Condor to sense and avoid threats autonomously Still, developments such as Agile Condor will significantly reduce the amount of necessary human interaction in various parts of the targeting process, as well as general intelligence collection and initial analysis, and potentially much more, as time goes on. It could also fuse various forms of sensor data and other available intelligence together to specifically weight possible areas of interest over others and prioritize certain targets. The Air Force has also said that this system could use these capabilities to enable drones to navigate and detect and avoid potential threats automatically, including at times when its links to a control center or the GPS satellite navigation system are disrupted or blocked entirely. Sources: Press Release; The Drive https://www.uasvision.com/2020/09/07/ga-asi-demonstrates-ai-driven-targeting-computer-with-afrls-agile-condor-pod/
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28 mars 2023 | International, Aérospatial, C4ISR
US Army Selects Northrop Grumman, Teamed with Shield AI, for Future Tactical Unmanned Aircraft System Prototype
Northrop Grumman has been chosen by the U.S. Army to participate in Increment 2 of the Future Tactical Unmanned Aircraft System (FTUAS) program.