843 research outputs found
Adaptive, fast walking in a biped robot under neuronal control and learning
Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks
Motion Planning and Control for the Locomotion of Humanoid Robot
This thesis aims to contribute on the motion planning and control problem of the locomotion
of humanoid robots. For the motion planning, various methods were proposed
in different levels of model dependence. First, a model free approach was proposed
which utilizes linear regression to estimate the relationship between foot placement
and moving velocity. The data-based feature makes it quite robust to handle modeling
error and external disturbance. As a generic control philosophy, it can be applied to
various robots with different gaits. To reduce the risk of collecting experimental data
of model-free method, based on the simplified linear inverted pendulum model, the
classic planning method of model predictive control was explored to optimize CoM
trajectory with predefined foot placements or optimize them two together with respect
to the ZMP constraint. Along with elaborately designed re-planning algorithm and
sparse discretization of trajectories, it is fast enough to run in real time and robust
enough to resist external disturbance. Thereafter, nonlinear models are utilized for
motion planning by performing forward simulation iteratively following the multiple
shooting method. A walking pattern is predefined to fix most of the degrees of the
robot, and only one decision variable, foot placement, is left in one motion plane and
therefore able to be solved in milliseconds which is sufficient to run in real time. In
order to track the planned trajectories and prevent the robot from falling over, diverse
control strategies were proposed according to the types of joint actuators. CoM stabilizer
was designed for the robots with position-controlled joints while quasi-static
Cartesian impedance control and optimization-based full body torque control were
implemented for the robots with torque-controlled joints. Various scenarios were set
up to demonstrate the feasibility and robustness of the proposed approaches, like
walking on uneven terrain, walking with narrow feet or straight leg, push recovery
and so on
Anticipatory Effects on Lower Extremity Neuromechanics During a Cutting Task
Context: Continued research into the mechanism of noncontact anterior cruciate ligament injury helps to improve clinical interventions and injury-prevention strategies. A better understanding of the effects of anticipation on landing neuromechanics may benefit training interventions.
Objective: To determine the effects of anticipation on lower extremity neuromechanics during a single-legged land-and-cut task.
Design: Controlled laboratory study.
Setting: University biomechanics laboratory.
Participants: Eighteen female National Collegiate Athletic Association Division I collegiate soccer players (age = 19.7 ± 0.8 years, height = 167.3 ± 6.0 cm, mass = 66.1 ± 2.1 kg).
Intervention(s): Participants performed a single-legged land-and-cut task under anticipated and unanticipated conditions.
Main Outcome Measure(s): Three-dimensional initial contact angles, peak joint angles, and peak internal joint moments and peak vertical ground reaction forces and sagittal-plane energy absorption of the 3 lower extremity joints; muscle activation of selected hip- and knee-joint muscles.
Results: Unanticipated cuts resulted in less knee flexion at initial contact and greater ankle toe-in displacement. Unanticipated cuts were also characterized by greater internal hip-abductor and external-rotator moments and smaller internal knee-extensor and external-rotator moments. Muscle-activation profiles during unanticipated cuts were associated with greater activation of the gluteus maximus during the precontact and landing phases.
Conclusions: Performing a cutting task under unanticipated conditions changed lower extremity neuromechanics compared with anticipated conditions. Most of the observed changes in lower extremity neuromechanics indicated the adoption of a hip-focused strategy during the unanticipated condition
Fast biped walking with a neuronal controller and physical computation
Biped walking remains a difficult problem and robot models can
greatly {facilitate} our understanding of the underlying
biomechanical principles as well as their neuronal control. The
goal of this study is to specifically demonstrate that stable
biped walking can be achieved by combining the physical properties
of the walking robot with a small, reflex-based neuronal network,
which is governed mainly by local sensor signals. This study shows
that human-like gaits emerge without {specific} position or
trajectory control and that the walker is able to compensate small
disturbances through its own dynamical properties. The reflexive
controller used here has the following characteristics, which are
different from earlier approaches: (1) Control is mainly local.
Hence, it uses only two signals (AEA=Anterior Extreme Angle and
GC=Ground Contact) which operate at the inter-joint level. All
other signals operate only at single joints. (2) Neither position
control nor trajectory tracking control is used. Instead, the
approximate nature of the local reflexes on each joint allows the
robot mechanics itself (e.g., its passive dynamics) to contribute
substantially to the overall gait trajectory computation. (3) The
motor control scheme used in the local reflexes of our robot is
more straightforward and has more biological plausibility than
that of other robots, because the outputs of the motorneurons in
our reflexive controller are directly driving the motors of the
joints, rather than working as references for position or velocity
control. As a consequence, the neural controller and the robot
mechanics are closely coupled as a neuro-mechanical system and
this study emphasises that dynamically stable biped walking gaits
emerge from the coupling between neural computation and physical
computation. This is demonstrated by different walking
experiments using two real robot as well as by a Poincar\'{e} map
analysis applied on a model of the robot in order to assess its
stability. In addition, this neuronal control structure allows the
use of a policy gradient reinforcement learning algorithm to tune
the parameters of the neurons in real-time, during walking. This
way the robot can reach a record-breaking walking speed of 3.5
leg-lengths per second after only a few minutes of online
learning, which is even comparable to the fastest relative speed
of human walking
Push recovery with stepping strategy based on time-projection control
In this paper, we present a simple control framework for on-line push
recovery with dynamic stepping properties. Due to relatively heavy legs in our
robot, we need to take swing dynamics into account and thus use a linear model
called 3LP which is composed of three pendulums to simulate swing and torso
dynamics. Based on 3LP equations, we formulate discrete LQR controllers and use
a particular time-projection method to adjust the next footstep location
on-line during the motion continuously. This adjustment, which is found based
on both pelvis and swing foot tracking errors, naturally takes the swing
dynamics into account. Suggested adjustments are added to the Cartesian 3LP
gaits and converted to joint-space trajectories through inverse kinematics.
Fixed and adaptive foot lift strategies also ensure enough ground clearance in
perturbed walking conditions. The proposed structure is robust, yet uses very
simple state estimation and basic position tracking. We rely on the physical
series elastic actuators to absorb impacts while introducing simple laws to
compensate their tracking bias. Extensive experiments demonstrate the
functionality of different control blocks and prove the effectiveness of
time-projection in extreme push recovery scenarios. We also show self-produced
and emergent walking gaits when the robot is subject to continuous dragging
forces. These gaits feature dynamic walking robustness due to relatively soft
springs in the ankles and avoiding any Zero Moment Point (ZMP) control in our
proposed architecture.Comment: 20 pages journal pape
Biologically inspired locomotion control of bipedal robot
Master'sMASTER OF ENGINEERIN
Simple models of legged locomotion based on compliant limb behavior = Grundmodelle pedaler Lokomotion basierend auf nachgiebigem Beinverhalten
In der vorliegenden Dissertation werden einfache Modelle zur Beinlokomotion unter der gemeinsamen Hypothese entwickelt, dass die beiden grundlegenden und als verschieden angesehenen Gangarten Gehen und Rennen auf ein allgemeines Konzept zurückgeführt werden können, welches in den Standphasen allein auf nachgiebigem Beinverhalten beruht. Hierbei wird auf der Ebene der mechanischen Beschreibung der Gangarten nachgiebiges Beinverhalten mittels des vom Rennen bekannten Masse-Feder-Modells abstrahiert. Zunächst wird eine vergleichsweise einfache, analytische Näherungslösung desselben identifiziert; in einem weiteren Schritt wird die charakteristische Geschwindigkeit des Gangartwechsels aus federartigem Beinverhalten erklärt; und schließlich wird ein zweibeiniges Masse-Feder-Modell für Gehen vorgeschlagen, welches die beobachteten Bodenreaktionskräfte dieser Gangart beschreibt. Auf der Ebene der neuromechanischen Beschreibung wird aufgezeigt, wie das mit einer mechanischen Feder abstrahierte Beinverhalten durch eine positive Rückkopplung der Muskelkraft dezentral und autonom innerhalb des Muskelskelettapparats erzeugt werden kann. Schließlich werden die Einzelergebnisse der Arbeit zusammengefasst, wobei die beiden fundamentalen Gangarten Gehen und Rennen innerhalb des zweibeinigen Masse-Feder-Modells vereinigt werden und die Bedeutung dieses, auf nachgiebigem Beinverhalten beruhenden Zusammenschlusses sowohl für die biomechanische und motorische Grundlagenforschung als auch für Anwendungen in der Robotik, Rehabilitation und Prothetik erörtert wird
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