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Despite the commercial self-driving car industry making rapid advances in on-road autonomous mobility, military off-road autonomy algorithms and simulation capability development have lagged due to the complexity of the off-road problem.
While on-road simulations focus on well-structured and predictable environments with few obstacles, the military off-road environment is more challenging and complex, with 3D surfaces, dirt, mud, and vegetation, hundreds of obstacles, bad maps, continuous motion planning, and no defined road networks or driving rules. The practical use of simulation in off-road robotics is limited because simulators are poor at modeling ground cover, broken terrain, and how ground vehicles can overcome vegetation obstacles.
According to militaryaerospace.com, a certain research program may change that state of affairs. US military researchers are asking industry to advance simulation technologies related to off-road machine autonomy to reduce the cost of developing off-road unmanned ground vehicles (UGVs) and bridge the gap from simulation to the real world.
Officials of the DARPA issued a broad agency announcement for the Robotic Autonomy in Complex Environments with Resiliency-Simulation (RACER-Sim) project. This project is closely related to the DARPA RACER program to develop machine autonomy that enables unmanned ground combat vehicles to maneuver safely over rough off-road terrain at speeds a human driver could achieve. Such a vehicle should be able to operate quickly over unstructured off-road terrain at speeds limited not by the autonomy software or processing time, but only by onboard sensor limitations, vehicle mechanical limits.
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