Why Did My Robot Choose This Path? Explainable Path Planning for Off-Road Navigation

In the field of off-road navigation, where traditional maps often fall short, intuitive and efficient path planning is essential for autonomous off-road vehicles. Navigating in off-road terrain poses unique challenges, requiring innovative solutions for users to understand and trust path suggestions made by an autonomous system. In this paper, we explore the integration of Explainable AI into off-road navigation systems to better understand the complexity of off-road environments. Our research introduces a method tailored to generate contextual explanations for chosen paths using terrain features, environmental factors, and robot capabilities. By combining inverse optimization techniques with shortest path algorithms, our approach aims to answer the question "Why is path $p^*$ recommended over path $p'$, which was expected by the user?" These explanations aim to shed light on the process of a robot's path planning task, focusing on elevation changes, terrain obstacles, and optimal path choices, thus improving the user's understanding of the chosen paths. A short user study evaluating the provided explanations generated in different off-road environments validates the effectiveness of our explanation algorithm and shows that it contributes to understanding the planning process of off-road navigation systems.