Terrain Dependent Power Estimation for Legged Robots in Unstructured Environments

Gait-based legged robots offer substantial advantages for traversing complicated, unstructured, or discontinuous terrain. Thus increasing their use in many real-world applications. However, they are also challenging to deploy due to limitations in operation time, range, and payload capabilities due to their complex locomotion and power needs. Anticipating the impact of terrain transitions on the range and average power consumption is crucial for understanding operational limits in autonomous and teleoperated missions. This study examines strategies for forecasting terrain-dependent energy costs on five unique surfaces (asphalt, concrete, grass, brush, and snow). The field experiments demonstrate the effectiveness of our combined proprioception and vision approach called MEP-VP. This hybrid framework only requires two seconds of motion data before returning actionable power estimates. Validation is conducted on physical hardware in field demonstration.