2,529 research outputs found
An SVD approach to reaching tasks based on cartesian geodesics
We are interested in human motion characterization and automatic motion simulation. The apparent redundancy of the humanoid w.r.t its explicit tasks lead to the problem of choosing a plausible movement in the framework of redundant kinematics. This work explores the intrinsic relationships between singular value decomposition at kinematic level and optimization principles at task level and joint level. The ideas are tested on sitting reach motions, for both translations and rotations task components
Design, analysis, and control of a cable-driven parallel platform with a pneumatic muscle active support
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The neck is an important part of the body that connects the head to the torso, supporting the weight and generating the movement of the head. In this paper, a cable-driven parallel platform with a pneumatic muscle active support (CPPPMS) is presented for imitating human necks, where cable actuators imitate neck muscles and a pneumatic muscle actuator imitates spinal muscles, respectively. Analyzing the stiffness of the mechanism is carried out based on screw theory, and this mechanism is optimized according to the stiffness characteristics. While taking the dynamics of the pneumatic muscle active support into consideration as well as the cable dynamics and the dynamics of the Up-platform, a dynamic modeling approach to the CPPPMS is established. In order to overcome the flexibility and uncertainties amid the dynamic model, a sliding mode controller is investigated for trajectory tracking, and the stability of the control system is verified by a Lyapunov function. Moreover, a PD controller is proposed for a comparative study. The results of the simulation indicate that the sliding mode controller is more effective than the PD controller for the CPPPMS, and the CPPPMS provides feasible performances for operations under the sliding mode control
Dynamic whole-body motion generation under rigid contacts and other unilateral constraints
The most widely used technique for generating wholebody motions on a humanoid robot accounting for various tasks and constraints is inverse kinematics. Based on the task-function approach, this class of methods enables the coordination of robot movements to execute several tasks in parallel and account for the sensor feedback in real time, thanks to the low computation cost.
To some extent, it also enables us to deal with some of the robot constraints (e.g., joint limits or visibility) and manage the quasi-static balance of the robot. In order to fully use the whole range of possible motions, this paper proposes extending the task-function approach to handle the full dynamics of the robot multibody along with any constraint written as equality or inequality of the state and control variables. The definition of multiple objectives is made possible by ordering them inside a strict hierarchy. Several models of contact with the environment can be implemented in the framework. We propose a reduced formulation of the multiple rigid planar contact that keeps a low computation cost. The efficiency of this approach is illustrated by presenting several multicontact dynamic motions in simulation and on the real HRP-2 robot
State Estimation for a Humanoid Robot
This paper introduces a framework for state estimation on a humanoid robot
platform using only common proprioceptive sensors and knowledge of leg
kinematics. The presented approach extends that detailed in [1] on a quadruped
platform by incorporating the rotational constraints imposed by the humanoid's
flat feet. As in previous work, the proposed Extended Kalman Filter (EKF)
accommodates contact switching and makes no assumptions about gait or terrain,
making it applicable on any humanoid platform for use in any task. The filter
employs a sensor-based prediction model which uses inertial data from an IMU
and corrects for integrated error using a kinematics-based measurement model
which relies on joint encoders and a kinematic model to determine the relative
position and orientation of the feet. A nonlinear observability analysis is
performed on both the original and updated filters and it is concluded that the
new filter significantly simplifies singular cases and improves the
observability characteristics of the system. Results on simulated walking and
squatting datasets demonstrate the performance gain of the flat-foot filter as
well as confirm the results of the presented observability analysis.Comment: IROS 2014 Submission, IEEE/RSJ International Conference on
Intelligent Robots and Systems (2014) 952-95
Learning Singularity Avoidance
With the increase in complexity of robotic systems and the rise in non-expert
users, it can be assumed that task constraints are not explicitly known. In
tasks where avoiding singularity is critical to its success, this paper
provides an approach, especially for non-expert users, for the system to learn
the constraints contained in a set of demonstrations, such that they can be
used to optimise an autonomous controller to avoid singularity, without having
to explicitly know the task constraints. The proposed approach avoids
singularity, and thereby unpredictable behaviour when carrying out a task, by
maximising the learnt manipulability throughout the motion of the constrained
system, and is not limited to kinematic systems. Its benefits are demonstrated
through comparisons with other control policies which show that the constrained
manipulability of a system learnt through demonstration can be used to avoid
singularities in cases where these other policies would fail. In the absence of
the systems manipulability subject to a tasks constraints, the proposed
approach can be used instead to infer these with results showing errors less
than 10^-5 in 3DOF simulated systems as well as 10^-2 using a 7DOF real world
robotic system
Momentum Control of Humanoid Robots with Series Elastic Actuators
Humanoid robots may require a degree of compliance at the joint level for
improving efficiency, shock tolerance, and safe interaction with humans. The
presence of joint elasticity, however, complexifies the design of balancing and
walking controllers. This paper proposes a control framework for extending
momentum based controllers developed for stiff actuators to the case of series
elastic actuators. The key point is to consider the motor velocities as an
intermediate control input, and then apply high-gain control to stabilise the
desired motor velocities achieving momentum control. Simulations carried out on
a model of the robot iCub verify the soundness of the proposed approach
3LP: a linear 3D-walking model including torso and swing dynamics
In this paper, we present a new model of biped locomotion which is composed
of three linear pendulums (one per leg and one for the whole upper body) to
describe stance, swing and torso dynamics. In addition to double support, this
model has different actuation possibilities in the swing hip and stance ankle
which could be widely used to produce different walking gaits. Without the need
for numerical time-integration, closed-form solutions help finding periodic
gaits which could be simply scaled in certain dimensions to modulate the motion
online. Thanks to linearity properties, the proposed model can provide a
computationally fast platform for model predictive controllers to predict the
future and consider meaningful inequality constraints to ensure feasibility of
the motion. Such property is coming from describing dynamics with joint torques
directly and therefore, reflecting hardware limitations more precisely, even in
the very abstract high level template space. The proposed model produces
human-like torque and ground reaction force profiles and thus, compared to
point-mass models, it is more promising for precise control of humanoid robots.
Despite being linear and lacking many other features of human walking like CoM
excursion, knee flexion and ground clearance, we show that the proposed model
can predict one of the main optimality trends in human walking, i.e. nonlinear
speed-frequency relationship. In this paper, we mainly focus on describing the
model and its capabilities, comparing it with human data and calculating
optimal human gait variables. Setting up control problems and advanced
biomechanical analysis still remain for future works.Comment: Journal paper under revie
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