322 research outputs found
Biped dynamic walking using reinforcement learning
This thesis presents a study of biped dynamic walking using reinforcement learning. A hardware biped robot was built. It uses low gear ratio DC motors in order to provide free leg movements. The Self Scaling Reinforcement learning algorithm was developed in order to deal with the problem of reinforcement learning in continuous action domains. A new learning architecture was designed to solve complex control problems. It uses different modules that consist of simple controllers and small neural networks. The architecture allows for easy incorporation of modules that represent new knowledge, or new requirements for the desired task. Control experiments were carried out using a simulator and the physical biped. The biped learned dynamic walking on flat surfaces without any previous knowledge about its dynamic model
Control of A High Performance Bipedal Robot using Viscoelastic Liquid Cooled Actuators
This paper describes the control, and evaluation of a new human-scaled biped
robot with liquid cooled viscoelastic actuators (VLCA). Based on the lessons
learned from previous work from our team on VLCA [1], we present a new system
design embodying a Reaction Force Sensing Series Elastic Actuator (RFSEA) and a
Force Sensing Series Elastic Actuator (FSEA). These designs are aimed at
reducing the size and weight of the robot's actuation system while inheriting
the advantages of our designs such as energy efficiency, torque density, impact
resistance and position/force controllability. The system design takes into
consideration human-inspired kinematics and range-of-motion (ROM), while
relying on foot placement to balance. In terms of actuator control, we perform
a stability analysis on a Disturbance Observer (DOB) designed for force
control. We then evaluate various position control algorithms both in the time
and frequency domains for our VLCA actuators. Having the low level baseline
established, we first perform a controller evaluation on the legs using
Operational Space Control (OSC) [2]. Finally, we move on to evaluating the full
bipedal robot by accomplishing unsupported dynamic walking by means of the
algorithms to appear in [3].Comment: 8 pages, 8 figure
Gait-Behavior Optimization Considering Arm Swing and Toe Mechanisms for Biped Robot on Rough Road
芝浦工業大学2019年
Stair Climbing using the Angular Momentum Linear Inverted Pendulum Model and Model Predictive Control
A new control paradigm using angular momentum and foot placement as state
variables in the linear inverted pendulum model has expanded the realm of
possibilities for the control of bipedal robots. This new paradigm, known as
the ALIP model, has shown effectiveness in cases where a robot's center of mass
height can be assumed to be constant or near constant as well as in cases where
there are no non-kinematic restrictions on foot placement. Walking up and down
stairs violates both of these assumptions, where center of mass height varies
significantly within a step and the geometry of the stairs restrict the
effectiveness of foot placement. In this paper, we explore a variation of the
ALIP model that allows the length of the virtual pendulum formed by the robot's
stance foot and center of mass to follow smooth trajectories during a step. We
couple this model with a control strategy constructed from a novel combination
of virtual constraint-based control and a model predictive control algorithm to
stabilize a stair climbing gait that does not soley rely on foot placement.
Simulations on a 20-degree of freedom model of the Cassie biped in the
SimMechanics simulation environment show that the controller is able to achieve
periodic gait
Globally stable control of a dynamic bipedal walker using adaptive frequency oscillators
We present a control method for a simple limit-cycle bipedal walker that uses adaptive frequency oscillators (AFOs) to generate stable gaits. Existence of stable limit cycles is demonstrated with an inverted-pendulum model. This model predicts a proportional relationship between hip torque amplitude and stride frequency. The closed-loop walking control incorporates adaptive Fourier analysis to generate a uniform oscillator phase. Gait solutions (fixed points) are predicted via linearization of the walker model, and employed as initial conditions to generate exact solutions via simulation. Global stability is determined via a recursive algorithm that generates the approximate basin of attraction of a fixed point. We also present an initial study on the implementation of AFO-based control on a bipedal walker with realistic mass distribution and articulated knee joint
Generating walking behaviours in legged robots
Many legged robots have boon built with a variety of different abilities, from running
to liopping to climbing stairs. Despite this however, there has been no consistency of
approach to the problem of getting them to walk. Approaches have included breaking
down the walking step into discrete parts and then controlling them separately, using
springs and linkages to achieve a passive walking cycle, and even working out the
necessary movements in simulation and then imposing them on the real robot. All of
these have limitations, although most were successful at the task for which they were
designed. However, all of them fall into one of two categories: either they alter the
dynamics of the robots physically so that the robot, whilst very good at walking, is
not as general purpose as it once was (as with the passive robots), or they control the
physical mechanism of the robot directly to achieve their goals, and this is a difficult
task.In this thesis a design methodology is described for building controllers for 3D dynam¬
ically stable walking, inspired by the best walkers and runners around — ourselves —
so the controllers produced are based 011 the vertebrate Central Nervous System. This
means that there is a low-level controller which adapts itself to the robot so that, when
switched on, it can be considered to simulate the springs and linkages of the passive
robots to produce a walking robot, and this now active mechanism is then controlled
by a relatively simple higher level controller. This is the best of both worlds — we
have a robot which is inherently capable of walking, and thus is easy to control like
the passive walkers, but also retains the general purpose abilities which makes it so
potentially useful.This design methodology uses an evolutionary algorithm to generate low-level control¬
lers for a selection of simulated legged robots. The thesis also looks in detail at previous
walking robots and their controllers and shows that some approaches, including staged
evolution and hand-coding designs, may be unnecessary, and indeed inappropriate, at
least for a general purpose controller. The specific algorithm used is evolutionary, using
a simple genetic algorithm to allow adaptation to different robot configurations, and
the controllers evolved are continuous time neural networks. These are chosen because
of their ability to entrain to the movement of the robot, allowing the whole robot and
network to be considered as a single dynamical system, which can then be controlled
by a higher level system.An extensive program of experiments investigates the types of neural models and net¬
work structures which are best suited to this task, and it is shown that stateless and
simple dynamic neural models are significantly outperformed as controllers by more
complex, biologically plausible ones but that other ideas taken from biological systems,
including network connectivities, are not generally as useful and reasons for this are
examined.The thesis then shows that this system, although only developed 011 a single robot,
is capable of automatically generating controllers for a wide selection of different test
designs. Finally it shows that high level controllers, at least to control steering and
speed, can be easily built 011 top of this now active walking mechanism
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