Mobile Robot Traversability Learning for High-Risk Terrain

The patent introduces a feedback mechanism where the robot actively seeks out high-risk terrains based on its current model uncertainty. The uncertainty estimation from the traversability model feeds into an active learning module that identifies underrepresented high-risk areas, which then become targets for prioritized exploration and data collection. This closed-loop feedback system continuously improves model reliability for high-risk terrains without requiring manual labels.

The patent performs preliminary risk assessment and uncertainty estimation during the training phase to identify which high-risk terrains are underrepresented in the current dataset. This preliminary analysis enables the system to proactively plan exploration routes that target these specific high-risk areas before actual navigation occurs, ensuring that critical data is collected in advance rather than reacting to failures.

The patent enables the robot to autonomously identify and explore high-risk terrains without human intervention. The system uses its own traversability model and uncertainty estimation to self-determine which hazardous areas need exploration, eliminating the driver's psychological bias. The robot serves itself by autonomously planning routes that target underexplored high-risk regions based on its internal model state.

The patent implements dynamic route planning that adapts in real-time based on the robot's current traversability model and identified data gaps. Rather than following fixed pre-planned routes or human-driven paths, the system dynamically adjusts navigation to prioritize high-risk areas that are underrepresented in the training data, ensuring balanced exploration across all terrain types.

The patent performs preliminary risk assessment and simulation-based validation before deploying active learning in real-world safety-critical applications. The system uses simulation environments to pre-train and validate the active learning module, identifying potential safety risks in advance. This preliminary action in controlled simulation environments allows the system to learn hazardous terrain traversal strategies without exposing the physical robot to actual safety risks.

The patent implements multiple layers of safety mechanisms that act as cushioning before actual active learning exploration occurs. These include simulation-based pre-training to expose the model to extreme scenarios without real-world risks, uncertainty thresholds that prevent exploration when confidence is too low, and hierarchical validation that requires simulation success before real-world deployment. These beforehand cushioning measures protect against safety risks while enabling improved traversability prediction.