scholarly journals Dynamic Walking on Compliant and Uneven Terrain using DCM and Passivity-based Whole-body Control

Author(s):  
George Mesesan ◽  
Johannes Englsberger ◽  
Gianluca Garofalo ◽  
Christian Ott ◽  
Alin Albu-Schaffer
2015 ◽  
Vol 12 (03) ◽  
pp. 1550027 ◽  
Author(s):  
Michael A. Hopkins ◽  
Dennis W. Hong ◽  
Alexander Leonessa

This paper presents a framework for dynamic walking on uneven terrain using a novel time-varying extension of the divergent component of motion (DCM). By varying the natural frequency of the DCM, we are able to achieve generic CoM height trajectories during stepping. The proposed approach computes admissible DCM reference trajectories given desired zero moment point (ZMP) plans for single and double support, permitting both flat-footed and heel-toe walking. Real-time planning is accomplished using reverse-time integration of the discretized DCM dynamics over a finite time horizon. To account for discontinuities during replanning, linear model predictive control (MPC) is implemented over a short preview window, enabling smooth transitions between steps. DCM tracking control is achieved using a time-varying proportional-integral controller based on the virtual repellent point (VRP). The effectiveness of the combined approach is verified in simulation using a 30 DOF model of THOR, a compliant torque-controlled humanoid. We demonstrate dynamic locomotion on uneven terrain and heel-toe walking using a complementary whole-body controller to track the corresponding VRP forces.


2011 ◽  
Vol 30 (3) ◽  
pp. 265-279 ◽  
Author(s):  
Ian R Manchester ◽  
Uwe Mettin ◽  
Fumiya Iida ◽  
Russ Tedrake

2013 ◽  
Vol 32 (9-10) ◽  
pp. 1089-1103 ◽  
Author(s):  
Sébastien Dalibard ◽  
Antonio El Khoury ◽  
Florent Lamiraux ◽  
Alireza Nakhaei ◽  
Michel Taïx ◽  
...  

2009 ◽  
Vol 21 (3) ◽  
pp. 311-316 ◽  
Author(s):  
Kensuke Harada ◽  
◽  
Mitsuharu Morisawa ◽  
Shin-ichiro Nakaoka ◽  
Kenji Kaneko ◽  
...  

For the purpose of realizing the humanoid robot walking on uneven terrain, this paper proposes the kinodynamic gait planning method where both kinematics and dynamics of the system are considered. We can simultaneously plan both the foot-place and the whole-body motion taking the dynamical balance of the robot into consideration. As a dynamic constraint, we consider the differential equation of the robot's CoG. To solve this constraint, we use a walking pattern generator. We randomly sample the configuration space to search for the path connecting the start and the goal configurations. To show the effectiveness of the proposed methods, we show simulation and experimental results where the humanoid robot HRP-2 walks on rocky cliff with hands contacting the environment.


2013 ◽  
Vol 10 (03) ◽  
pp. 1350027 ◽  
Author(s):  
PATRICK M. WENSING ◽  
GHASSAN BIN HAMMAM ◽  
BEHZAD DARIUSH ◽  
DAVID E. ORIN

The force distribution problem (FDP) in robotics requires the determination of multiple contact forces to match a desired net contact wrench. For the double support case encountered in humanoids, this problem is underspecified, and provides the opportunity to optimize desired foot centers of pressure (CoPs) and forces. In different contexts, we may seek CoPs and contact forces that optimize actuator effort or decrease the tendency for foot roll. In this work, we present two formulations of the FDP for humanoids in double support, and propose objective functions within a general framework to address the variety of competing requirements for the realization of balance. As a key feature, the framework is capable to optimize contact forces for motions on uneven terrain. Solutions for the formulations developed are obtained with a commercial nonlinear optimization package and through analytical approaches on a simplified problem. Results are shown for a highly dynamic whole-body humanoid reaching motion performed on even terrain and on a ramp. A convex formulation of the FDP provides real-time solutions with computation times of a few milliseconds. While the convex formulation does not include CoPs explicitly as optimization variables, a novel objective function is developed which penalizes foot CoP solutions that approach the foot boundaries.


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