Measurement and Prediction of Override Force of Clumps of Small Vegetation in Off-Road Autonomous Navigation

Autonomous navigation in off-road terrain requires a perception system that can distinguish between vegetation that can easily be overridden and vegetation that cannot. While many autonomous systems struggle to estimate the navigability of vegetation like sparse grass or small shrubs, in this work we use a new vehicle-embedded force sensor to directly measure override forces as the vehicle drives through vegetation, allowing the perception system to learn the navigability of vegetation based on the corresponding sensor signatures. The override force can be estimated using a neural network trained on a combination of lidar and images, and the resulting force prediction can be used as an input into both local and global path-planning algorithms for autonomous navigation. In this work, we show the results for our force measurements and outline the process for extracting training data to predict override force using RESNET-50.