Humanoid Walking System with CNN-based Uneven Terrain Recognition and Landing Control with Swing-leg Velocity Constraints

In order for a humanoid robot to traverse uneven terrain without falling over, the robot must control its landing position appropriately. To determine the landing position, there are two difficulties in terrain recognition and leg motion control. In terrain recognition, it is difficult to recognize and avoid terrain such as steps and obstacles that cannot be landed on in real-time. In leg motion control, it is necessary to land at appropriate positions and times to control the CoG trajectory while limiting the velocity of the swing-leg to suppress the landing impact. For solving these problems, we propose a recognition and walking control system on uneven terrain. In terrain recognition, we improved the recognition accuracy while satisfying real-time performance by using a CNN that learns the relationship between the foot and the geometric information of the surrounding terrain. In the leg motion control, landing impact was reduced by modifying the landing position under not only (1) terrain constraint and (2) robot stability constraint, but also (3) leg velocity constraint. We verified the effectiveness of the proposed system through uneven terrain walking and push recovery experiments using the actual robot.