A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

Dafarra

et al. 2019

Self Cite

We present a computationally efficient method for online planning of bipedal walking trajectories with push recovery. In particular, the proposed methodology fits control architectures where the Divergent-Component-of-Motion (DCM) is planned beforehand, and adds a step adapter to adjust the planned trajectories and achieve push recovery. Assuming that the robot is in a single support state, the step adapter generates new positions and timings for the next step. The step adapter is active in single support phases only, but the proposed torque-control architecture considers double support phases too. The key idea for the design of the step adapter is to impose both initial and final DCM step values using an exponential interpolation of the time varying ZMP trajectory. This allows us to cast the push recovery problem as a Quadratic Programming (QP) one, and to solve it online with stateof-the-art optimisers. The overall approach is validated with simulations of the torque-controlled 33 kg humanoid robot iCub. Results show that the proposed strategy prevents the humanoid robot from falling while walking at 0.28 m/s and pushed with external forces up to 150 Newton for 0.05 seconds.

Section: Introductionmentioning

confidence: 99%

Online DCM Trajectory Generation for Push Recovery of Torque-Controlled Humanoid Robots

Shafiee

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

Dafarra

et al. 2019

Self Cite

“…A torque-controlled humanoid robot is, in fact, intrinsically compliant in case of external unexpected interactions, and it can be thus used to perform cooperative tasks alongside humans. 20 This paper extends and encompasses our previous work 21 and presents and compares several DCM based implementations of the above layered control architecture. In particular, the trajectory optimization layer is kept fixed with a unicycle based planner that generates desired DCM and foot trajectories.…”

Section: Introductionmentioning

confidence: 66%

“…In our previous work, 21 the benchmarking versus the walking velocity was performed by considering the desired velocity set in the Trajectory Optimization layer. Since the Trajectory Optimization layer computes the desired trajectories solving an optimization problem, the actual planned velocity may be different from the velocity set in the layer.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Benchmarking of DCM-Based Architectures for Position, Velocity and Torque-Controlled Humanoid Robots

Dafarra

et al. 2020

Int. J. Human. Robot.

Self Cite

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.

“…So, concerning the lower body teleoperation of humanoid robots, the aspects of stability and locomotion have higher precedence over retargeting of all the lower limbs. A more detailed description of such methods are discussed in [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Whole-Body Geometric Retargeting for Humanoid Robots

Darvish

Tirupachuri

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

et al. 2019

Self Cite

Humanoid robot teleoperation allows humans to integrate their cognitive capabilities with the apparatus to perform tasks that need high strength, manoeuvrability and dexterity. This paper presents a framework for teleoperation of humanoid robots using a novel approach for motion retargeting through inverse kinematics over the robot model. The proposed method enhances scalability for retargeting, i.e., it allows teleoperating different robots by different human users with minimal changes to the proposed system. Our framework enables an intuitive and natural interaction between the human operator and the humanoid robot at the configuration space level. We validate our approach by demonstrating whole-body retargeting with multiple robot models. Furthermore, we present experimental validation through teleoperation experiments using two state-of-the-art whole-body controllers for humanoid robots.