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Adaptive locomotion in different types of surfaces is of critical importance for legged robots.The knowledge of various ground substrates,especially some geological properties,plays an essential role in ensuring the legged robots’safety.In this paper,the interaction between the robots and the environments is investigated through interaction dynamics with the closed-loop system model,the compliant contact model,and the friction model,which unveil the influence of environment’s geological characteristics for legged robots’locomotion.The proposed method to classify substrates is based on the interaction dynamics and the sensory-motor coordination.The foot contact forces,joint position errors,and joint motor currents,which reflect body dynamics,are measured as the sensing variables.We train and classify the features extracted from the raw data with a multilevel weighted k-Nearest Neighbor(kNN) algorithm.According to the interaction dynamics,the strategy of adaptive walking is developed by adjusting the touchdown angles and foot trajectories while lifting up and dropping down the foot.Experiments are conducted on five different substrates with quadruped robot FROG-I.The comparison with other classification methods and adaptive walking between different substrates demonstrate the effectiveness of our approach.
Adaptive locomotion in different types of surfaces is of critical importance for legged robots. The knowledge of various ground substrates, especially some geological properties, plays an essential role in ensuring the legged robots’safety. In this paper, the interaction between the robots and the environments is investigated through interaction dynamics with the closed-loop system model, the compliant contact model, and the friction model, which unveil the influence of environment’s geological characteristics for legged robots’locomotion. The proposed method to classify substrates is based on the interaction dynamics and the sensory-motor coordination. The foot contact forces, joint position errors, and joint motor currents, which reflect body dynamics, are measured as the sensing variables. We train and classify the features extracted from the raw data with a multilevel weighted k- Nearest Neighbor (kNN) algorithm. According to the interaction dynamics, the strategy of adaptive walking is developed by adjusting the touchdown angles and foot trajectories while lifting up and dropping down the foot. Experiments on five different substrates with quadruped robot FROG-I. the comparison with other classification methods and adaptive walking between different substrates demonstrate the effectiveness of our approach.