Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation
This addresses the challenge of consistent robot navigation in unstructured off-road environments like forests, which is crucial for applications such as disaster response, though it appears incremental as it builds on existing adaptation methods.
The paper tackles the problem of ground robots failing to match expected behaviors in off-road terrains due to terrain and robot changes, proposing a self-reflective terrain-aware adaptation method that enables consistent navigation and outperforms previous techniques in experiments.
Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method's performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.