人間を模擬した知覚系を持つ2足ヒューマノイドロボットの自己位置推定機能に対する歩行動作の影響に関する研究

早大学位記番号:新8279早稲田大

Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain

In this paper we tackle the problem of visually predicting surface friction for environments with diverse surfaces, and integrating this knowledge into biped robot locomotion planning. The problem is essential for autonomous robot locomotion since diverse surfaces with varying friction abound in the real world, from wood to ceramic tiles, grass or ice, which may cause difficulties or huge energy costs for robot locomotion if not considered. We propose to estimate friction and its uncertainty from visual estimation of material classes using convolutional neural networks, together with probability distribution functions of friction associated with each material. We then robustly integrate the friction predictions into a hierarchical (footstep and full-body) planning method using chance constraints, and optimize the same trajectory costs at both levels of the planning method for consistency. Our solution achieves fully autonomous perception and locomotion on slippery terrain, which considers not only friction and its uncertainty, but also collision, stability and trajectory cost. We show promising friction prediction results in real pictures of outdoor scenarios, and planning experiments on a real robot facing surfaces with different friction

Diseño, construcción y control de estabilidad de un robot que se blancea sobre una esfera

Diseño, construcción y control de estabilidad de un robot que se blancea sobre una esfer