Terrain adaptation is a critical ability for a ground robot to effectively traverse unstructured off-road terrain in real-world field environments such as forests. However, the expected or planned maneuvering behaviors cannot always be accurately executed due to setbacks such as reduced tire pressure. This inconsistency negatively affects the robot's ground maneuverability and can cause slower traversal time or errors in localization. To address this shortcoming, we propose a novel method for consistent behavior generation that enables a ground robot's actual behaviors to more accurately match expected behaviors while adapting to a variety of complex off-road terrains. Our method learns offset behaviors in a self-supervised fashion to compensate for the inconsistency between the actual and expected behaviors without requiring the explicit modeling of various setbacks. To evaluate the method, we perform extensive experiments using a physical ground robot over diverse complex off-road terrain in real-world field environments. Experimental results show that our method enables a robot to improve its ground maneuverability on complex unstructured off-road terrain with more navigational behavior consistency, and outperforms previous and baseline methods, particularly so on challenging terrain such as that which is seen in forests.
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Enhancing Consistent Ground Maneuverability by Robot Adaptation to Complex Off-Road Terrains
Terrain adaptation is a critical ability for a ground robot to effectively
traverse unstructured off-road terrain in real-world field environments such as
forests. However, the expected or planned maneuvering behaviors cannot always
be accurately executed due to setbacks such as reduced tire pressure. This inconsistency negatively affects the robot’s ground maneuverability, and can cause slower
traversal time or errors in localization. To address this shortcoming, we propose
a novel method for consistent behavior generation that enables a ground robot’s
actual behaviors to more accurately match expected behaviors while adapting to
a variety of complex off-road terrains. Our method learns offset behaviors in a
self-supervised fashion to compensate for the inconsistency between the actual and
expected behaviors without requiring the explicit modeling of various setbacks. To
evaluate the method, we perform extensive experiments using a physical ground
robot over diverse complex off-road terrain in real-world field environments. Experimental results show that our method enables a robot to improve its ground
maneuverability on complex unstructured off-road terrain with more navigational
behavior consistency, and outperforms previous and baseline methods, particularly
so on challenging terrain such as that which is seen in forests.
more »
« less
- Award ID(s):
- 1823245
- NSF-PAR ID:
- 10340542
- Date Published:
- Journal Name:
- Conference on Robot Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.
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