DARPA RACER Fleet Vehicles to test high-speed and off-road autonomy


DARPA’s Robotic Autonomy in Complex Environments with Resiliency (RACER) program is picking up speed after three teams were selected to go to the starting line last October. The program is focused on advancing off-road autonomy of combat vehicles and seeks to demonstrate the ability of these platforms to travel at speeds that maintain pace with manned combat vehicles in complex terrain typical of that seen in real situations. Awards for Phase 1 were given to Carnegie Mellon University, NASA-Jet Propulsion Laboratory, and University of Washington.
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RACER will demonstrate game-changing autonomous UGV mobility, focused on speed and resiliency, using a combination of simulation and advanced platforms. It tests algorithms in the field at DARPA-hosted experiments across the country on a variety of terrain. The program provides UGV platforms that research teams can use to develop autonomous software capabilities through repeated cycles of tests on unstructured off-road landscapes. Goals include not only autonomy algorithms, but also creation of simulation-based approaches and environments that will support rapid advancement of self-driving capabilities for future UGVs (Picture source: DARPA)


The DARPA RACER program’s goal is to develop and demonstrate autonomy technologies that enable unmanned ground vehicles (UGVs) to maneuver in unstructured off-road terrain at the limit of the vehicle’s mechanical systems and at, or beyond, human-driven speeds and efficiencies. “RACER is intended to disruptively advance the integration and fielding of autonomy for robotic combat vehicles into the Army, Marine Corps, and Special Forces communities,” said Stuart Young, RACER program manager in DARPA’s Tactical Technology Office.

In November, the selected teams received the first of the DARPA-provided RACER Fleet Vehicles (RFVs) – a high performance all-terrain vehicle outfitted with world-class sensing and computational abilities – that they are using to develop platform-based autonomy for testing at upcoming DARPA-hosted field experiments.

RFV robots include 360o range and image sensing such as multiple LIDARs, stereo camera pairs, color and infrared imaging cameras, RADAR, event sensors, and inertial measurement sensing. Computation tools have multiple best-of-class graphical processing units (GPUs) in an environmentally protected, shock/vibration proof, and thermally managed Electronics Box (E-Box) that’s specifically engineered for the demands of the RACER high speed, off-road terrain expected in DARPA’s tests.

The sensor and E-box combination currently collects four terabytes of sensor data per hour to support artificial intelligence, machine learning-based autonomy algorithms and stack approaches required of fast-paced combat maneuvers in complex terrain. Modifications for roll protection, sensor/E-box integration, autonomous control, and increased 7kW of power are included in each RFV. The RFVs were integrated by Carnegie Robotics LLC (CRL) and are housed on a Polaris RZR S4 1000 Turbo base drive-by-wire platform.


Early sensor-based visualizations of terrain acquired by RACER Fleet Vehicle data collections


Four RFVs have been completed, with three already delivered to RACER Phase 1 performers in November 2021. Four more are expected to be built prior to DARPA’s first RACER-hosted field experiment, scheduled for March of 2022 at the National Training Center in Ft. Irwin, California.

DARPA-hosted field experiments will provide the teams with a place to demonstrate the full capability of their autonomy stacks in complex environments. At Ft. Irwin, teams will demonstrate their ability to navigate courses with a variety of terrain and distances up to five kilometers.

To further support software development, DARPA has also collected over 100 terabytes of RFV-based sensor data from more than 500 kilometers of terrain in the Mid-Atlantic and West Coast. Shared with teams and managed within a RACER development tool for efficiency and security, this data will assist with learning approaches. Conceptual government baseline stacks and autonomy architectures will also be provided. They leverage recent products of Combat Capabilities Development Command-Army Research Laboratory initiatives in collaborative robotics, learning, and intelligent systems activities in partnership with basic research university and industry consortia.


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RFV robots include 360o range and image sensing such as multiple LIDARs, stereo camera pairs, color and infrared imaging cameras, RADAR, event sensors, and inertial measurement sensing.  (Picture source: DARPA)


The RACER program also has awarded two contracts to develop simulation environments and capabilities to enable development of off-road autonomy algorithms. “The RACER-SIM portion of the program looks to expand current simulation capabilities and physics-based models to support the testing of off-road autonomy in virtual environments,” said Young. “These simulation environments will allow teams to test and validate portions of their autonomy stacks without having to spend large amounts of time and money with field testing.”

The two primes for the RACER-SIM program are Duality Robotics and Intel-Federal.

Future RACER program activities include continuation into a RACER Phase 2 effort with a more representative 10T combat vehicle surrogate. The plan is to evolve in speed, scale, and mobility beyond the RFVs, as well as add a research track exploring tactics-based derivation of the new platforms.


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“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.” (Picture source: DARPA)


 

 

Defense News January 2022

 

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