Driverless technology could reduce the risk of injury or death for convoys traveling through territory with hidden roadside bombs, said Bernard Thiesen, technical manager for Autonomous Mobility Applique Systems at TARDEC. The Army believes the self-driving vehicles could be ideal during humanitarian relief missions in a natural disaster or for resupplying troops in the field, recognizing opportunities for cost savings and fewer crashes.
Autonomy-enabled systems will deploy as force multipliers at all echelons from the squad to the brigade combat teams. Future robotic technologies and unmanned ground systems (UGS) will augment Soldiers and increase unit capabilities, situational awareness, mobility, and speed of action. Artificial intelligence will enable the deployment of autonomous and semi-autonomous systems with the ability to learn. Decision aids will reduce the cognitive burden and help leaders make rapid decisions.
The self-driving car industry has made great autonomy advances, but mostly for well-structured and highly predictable environments. In complex militarily-relevant settings, robotic vehicles have not demonstrated operationally relevant speed and aren’t autonomously reliable. While vehicle platforms that can handle difficult terrain exist, their autonomy algorithms and software often can’t process and respond to changing situations well enough to maintain necessary speeds and keep up with soldiers on a mission.
“Unlike the commercial sector, we have to develop systems that can manoeuvre off-road, that can manoeuvre in all elements…. that can navigate obstacles, whether they be trees or gullies or rocks or whatever they may be,” US Secretary of the Army, Mark Esper said. Earlier, Michael Griffin, the undersecretary of defense for research and engineering had claimed that United States Army will have self-driving vehicles operating on the battlefield long before they’re on U.S. streets and highways, “But the core technologies will be the same.” “At a minimum, performance at par with a human driver should be achieved.”
DARPA launched Robotic Autonomy in Complex Environments with Resiliency (RACER) program in Oct 2020 with aim to make sure algorithms aren’t the limiting part of the system and that autonomous combat vehicles can meet or exceed soldier driving abilities. Over a four-year timeline, RACER will develop new algorithm technologies that maximize utilization of the sensor and mechanical limits of Unmanned Ground Vehicles (UGVs) and constantly test these algorithms in the field at DARPA-hosted experiments across the country on a variety of terrain. DARPA will provide advanced UGV platforms that research teams will use to develop autonomous software capabilities through repeated cycles of simulations and tests on unstructured off-road landscapes.
“In order to achieve RACER goals of increased speed and resilience, we need to embrace learning approaches that automatically tune system parameters in real time,” said Stuart Young, program manager leading the RACER project. “Successful software will extract features from sensor data and use that information to make on-the-spot driving decisions.”
RACER program goals include not only autonomy algorithms, but also creation of simulation environments that will support rapid advancement of self-driving capabilities for future UGVs. DARPA is slated to provide advanced UGV platforms research teams will use to develop autonomous software capabilities through repeated cycles of simulations and tests on unstructured off-road landscapes. Over a span of four years, RACER would develop new algorithm technologies maximizing utilization of the sensor and mechanical limits of UGVs while consistently testing the algorithms in the field at DARPA-hosted experiments nationwide on a variety of terrain.
The Army Reseach Lab has also been focusing on algorithms to advance UGVs. Researchers from ARL and the University of Texas at Austin are working on a suite of algorithms, libraries and software components that intelligent systems can use for navigation, planning, perception, control and reasoning when performing specific tasks. The goal is to teach ground robots to learn by doing, rather than responding to verbal commands, which will improve how autonomous systems move through rugged and unfamiliar terrain.
ARL’s Scalable, Adaptive and Resilient Autonomy program is looking to improve how autonomous ground systems travel through increasingly complex off-road environments. Software developed by participants will be integrated into testbed platforms and ARL’s autonomous systems software repository so it will be more broadly available.
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