A Benchmarking of DCM Based Architectures for Position, Velocity and Torque Controlled Humanoid Robots
This work addresses the problem of improving locomotion performance for humanoid robots, but it is incremental as it benchmarks existing DCM-based methods with minor variations.
This paper benchmarks control architectures for bipedal robot locomotion based on the Divergent Component of Motion, comparing implementations with position, velocity, and torque control modes. It shows that one architecture enables the iCub humanoid robot to achieve a forward walking velocity of 0.3372 meters per second, the highest recorded for this robot.
This paper contributes towards the benchmarking of control architectures for bipedal robot locomotion. It considers architectures that are based on the Divergent Component of Motion (DCM) and composed of three main layers: trajectory optimization, simplified model control, and whole-body QP control layer. While the first two layers use simplified robot models, the whole-body QP control layer uses a complete robot model to produce either desired positions, velocities, or torques inputs at the joint-level. This paper then compares two implementations of the simplified model control layer, which are tested with position, velocity, and torque control modes for the whole-body QP control layer. In particular, both an instantaneous and a Receding Horizon controller are presented for the simplified model control layer. We show also that one of the proposed architectures allows the humanoid robot iCub to achieve a forward walking velocity of 0.3372 meters per second, which is the highest walking velocity achieved by the iCub robot.