Integrated Optimization of Climbing Locomotion for a Humanoid Robot

Abstract

We investigate real-time motion generation for a humanoid robot climbing a wall. We apply nonlinear model predictive control (NMPC) to optimize the climbing path and motion of the limbs simultaneously. To climb a wall, the robot must determine which hold it will grasp in each step. Kinematic constraints vary depending on the holds grasped by the robot, and these constraints will change as the robot climbs. To determine which hold to grasp while considering kinematics constraints, our proposed method introduces a potential function on the wall and state-dependent weights. We also propose online configuration of the performance index for NMPC to reduce the computational cost. This enables optimization under a complicated wall model. Only information in the vicinity of the humanoid robot is utilized for optimization because the control input over a finite future horizon is optimized at each sampling time of NMPC. We simulate the behavior of a climbing robot to show that the computation time is nearly the same as a sampling period.