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To function at the same operational tempo as human teammates on the battlefield in a robust and resilient manner, autonomous systems must assess and manage risk as it pertains to vehicle navigation. Risk comes in multiple forms, associated with both specific and uncertain terrains, environmental conditions, and nearby actors. In this work, we present a risk-aware path planning method to handle the first form, incorporating perception uncertainty over terrain types to trade-off between exploration and exploitation behaviors. The uncertainty from machine learned terrain segmentation models is used to generate a layered terrain map that associates every grid cell with its label uncertainty among the semantic classes. The risk term increases when differently traversable semantic classes (e.g., tree and grass) are associated with the same cell. We show that adjusting risk tolerances allows the planner to recognize and generate paths through materials like tall grass that historically have been ruled out when only considering geometry. Utilizing a risk-aware planner allows triggering an exploratory behavior to gather more information to minimize uncertainty over terrain categorizations. Most existing methods for incorporating risk will avoid regions of uncertainty, whereas here the vehicle can determine if the risk is too high after new observation/investigation. This also allows the autonomous system to decide to ask a human teammate for help to reduce uncertainty and make progress towards goal. We demonstrate the approach on a ground robot in simulation and in real world for autonomously navigating through a wooded environment.
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