Traversability-aware Adaptive Optimization for Path Planning and Control in Mountainous Terrain

Autonomous navigation in extreme mountainous terrains poses challenges due to the presence of mobility-stressing elements and undulating surfaces, making it particularly difficult compared to conventional off-road driving scenarios. In such environments, estimating traversability solely based on exteroceptive sensors often leads to the inability to reach the goal due to a high prevalence of non-traversable areas. In this paper, we consider traversability as a relative value that integrates the robot's internal state, such as speed and torque to exhibit resilient behavior to reach its goal successfully. We separate traversability into apparent traversability and relative traversability, then incorporate these distinctions in the optimization process of sampling-based planning and motion predictive control. Our method enables the robots to execute the desired behaviors more accurately while avoiding hazardous regions and getting stuck. Experiments conducted on simulation with 27 diverse types of mountainous terrain and real-world demonstrate the robustness of the proposed framework, with increasingly better performance observed in more complex environments.

Follow the Footprints: Self-supervised Traversability Estimation for Off-road Vehicle Navigation based on Geometric and Visual Cues

In this study, we address the off-road traversability estimation problem, that predicts areas where a robot can navigate in off-road environments. An off-road environment is an unstructured environment comprising a combination of traversable and non-traversable spaces, which presents a challenge for estimating traversability. This study highlights three primary factors that affect a robot's traversability in an off-road environment: surface slope, semantic information, and robot platform. We present two strategies for estimating traversability, using a guide filter network (GFN) and footprint supervision module (FSM). The first strategy involves building a novel GFN using a newly designed guide filter layer. The GFN interprets the surface and semantic information from the input data and integrates them to extract features optimized for traversability estimation. The second strategy involves developing an FSM, which is a self-supervision module that utilizes the path traversed by the robot in pre-driving, also known as a footprint. This enables the prediction of traversability that reflects the characteristics of the robot platform. Based on these two strategies, the proposed method overcomes the limitations of existing methods, which require laborious human supervision and lack scalability. Extensive experiments in diverse conditions, including automobiles and unmanned ground vehicles, herbfields, woodlands, and farmlands, demonstrate that the proposed method is compatible for various robot platforms and adaptable to a range of terrains. Code is available at https://github.com/yurimjeon1892/FtFoot.