Abstract
In this paper we report results on benchmarking a HRP-2 humanoid robot. The humanoid robots of this serie are known to be very robust. They have been successfully used by several research groups for the design of new motion generation algorithms. As such it is a reference in the category of electrically driven humanoid robot. As new humanoid robots are continuously built it is interesting to compare the performances of these new prototypes to those of HRP-2. This benchmarking study was realized through a campaign of measurements in an advanced equipped testing laboratory that provides a well adapted controlled environment. We have investigated the effect of temperatures variation on the robot walking capabilities. In order to benchmark various environmental conditions and algorithms we computed a set of performance indicators for bipedal locomotion. The scope of the algorithms for motion generation evaluated here ranges from analytical solution to numerical optimization approach, enabling real-time walking or multi-contacts motions.
Highlights
The one used is from Koch et al (2014) using the Muscod-II Diehl et al (2001) nonlinear solver. In this paragraph we present the numerical results obtained from the computation of the Key Performance Indicators (KPI) explained in detail in section 3.7 for each set of experiments
Morisawa et al (2007) Centroidal Dynamics Pattern Generator (CDPG) was evaluated at the beginning of the project the variation of height violates the assumption of the cart-table model
In this paper we presented a benchmarking for the control architecture described in Figure 1 that was implemented on the HRP-2 robot owned by LAAS-CNRS
Summary
Based on an internal representation of the environment and the localization of the robot (rb and θb being, respectively, the base position and orientation), the Motion Planner (MP) plans a sequence of reference end-effector contact positions (f ref ), or a reference center of mass linear velocity combined with a reference waist angular velocity (Vref ) These references are provided to a Model-Predictive Whole-Body Controller (MPWBC) which generates a motor command for each joint (joint torques (τ ref ), positions (qref ), velocities (qref ) and accelerations (qref )). The interaction with the environment is provided by the force sensors classically located at the end-effectors (FEE ∈ {FRF, FLF, FRH, FLH}, where the subscripts have the following meaning: (EE): end-effector, (RF): right foot, (LF): left foot, (RH): right hand, (LH): left hand) All these information are treated in an Estimator to extract the needed values for the different algorithms.
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