Autonomous passenger-sized wheeled and tracked vehicles navigating in off-road terrain often encounter vegetation along their desired trajectory. Vehicles of this size may be able to drive over (override) small vegetation with little impact to mobility. It may also be possible to avoid the vegetation and take an alternate path. While human drivers weigh many competing factors (e.g. the discomfort of swerving to avoid small vegetation, the jerk associated with hitting medium sized vegetation, risk of damage to the vehicle, etc.) in real time to make navigation decisions around vegetation, autonomous drivers often struggle to make reasonable decisions about navigating vegetation in off-road environments. Up until now, autonomous vehicles lacked a vehicle-independent method for estimating the override resistance of vegetation from lidar and camera data. In the work, we show the results of our recent direct measurements of override forces on medium-sized passenger vehicles navigating through clumps of small vegetation using an integrated pushbar system. These measurements are the first of their kind for vehicles <2000 lbs. We use sensor fusion and machine learning to develop a predictive model for vegetation override resistance that uses fused monocular camera and lidar data. We also develop a convolutional neural network (CNN) that can predict override force based on aerial RGB imagery.


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    Title :

    Predicting vegetation override forces for path planning in offroad terrain


    Contributors:

    Conference:

    Unmanned Systems Technology XXVII ; 2025 ; Orlando, Florida, United States


    Published in:

    Proc. SPIE ; 13477 ; 1347705


    Publication date :

    2025-05-28





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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