36 research outputs found
Locomotion training of legged robots using hybrid machine learning techniques
In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results
A study on automatic gait parameter tuning for biped walking robots
Automatic gait parameter tuning for biped walking robots is the subject of this thesis. The biped structure is one of the most versatile ones for the employment of mobile robots in the human environment. Their control is challenging because of their many DOFs and nonlinearities in their dynamics. Open loop walking with offline walk pattern generation is one of the methods for walking control. in this method the reference positions of the foot centers with respect to the body center are generated as functionals. Commonly, the tuning process for the trajectory generation is based on numerous trial and error steps. Obviously, this is a time consuming and elaborate process. In this work, online adaptation schemes for one of the trajectory parameters, "z-reference asymmetry", which is used for the compensation of uneven weight distribution of the robot in the sagittal plane, is proposed. In one of the approaches presented, this parameter is tuned online. As an alternative to parameter tuning, a functional learning scheme employing fuzzy identifiers is tested too. Fuzzy identifiers are universal function approximators. Fuzzy system parameters are adapted via back-propagation. An on-line tuning scheme for biped walk parameters however can only be successful if there is sufficient time for training without falling. The training might last hundreds of reference cycles. This implies that a mechanism for keeping the robot in continuous walk, even when the parameter settings are totally wrong, is necessary during training. In this work, virtual torsional springs which resist against deviations of the robot trunk angles from zero, are attached to the trunk center of the biped. The torques generated by the springs serve as the criteria for the tuning and help in maintaining a stable and a longer walk. The springs are removed after training. This novel approach can be applied to a wide range of control systems that involve parameter tuning. 3-D simulation techniques using C++ are employed for the model of a 12-DOF biped robot to test the proposed adaptive method. in order to visualize the walking, simulation results are animated using an OpenGL based animation environment. As a result of the simulations, a functional for the desired parameter, keeping the system in balance while walking, is generated
Locomoção bípede adaptativa a partir de uma única demonstração usando primitivas de movimento
Doutoramento em Engenharia EletrotécnicaEste trabalho aborda o problema de capacidade de imitação da locomoção
humana através da utilização de trajetórias de baixo nível codificadas com
primitivas de movimento e utilizá-las para depois generalizar para novas
situações, partindo apenas de uma demonstração única. Assim, nesta linha de
pensamento, os principais objetivos deste trabalho são dois: o primeiro é
analisar, extrair e codificar demonstrações efetuadas por um humano, obtidas
por um sistema de captura de movimento de forma a modelar tarefas de
locomoção bípede. Contudo, esta transferência não está limitada à simples
reprodução desses movimentos, requerendo uma evolução das capacidades
para adaptação a novas situações, assim como lidar com perturbações
inesperadas. Assim, o segundo objetivo é o desenvolvimento e avaliação de
uma estrutura de controlo com capacidade de modelação das ações, de tal
forma que a demonstração única apreendida possa ser modificada para o robô
se adaptar a diversas situações, tendo em conta a sua dinâmica e o ambiente
onde está inserido.
A ideia por detrás desta abordagem é resolver o problema da generalização a
partir de uma demonstração única, combinando para isso duas estruturas
básicas. A primeira consiste num sistema gerador de padrões baseado em
primitivas de movimento utilizando sistemas dinâmicos (DS). Esta abordagem
de codificação de movimentos possui propriedades desejáveis que a torna ideal
para geração de trajetórias, tais como a possibilidade de modificar determinados
parâmetros em tempo real, tais como a amplitude ou a frequência do ciclo do
movimento e robustez a pequenas perturbações. A segunda estrutura, que está
embebida na anterior, é composta por um conjunto de osciladores acoplados
em fase que organizam as ações de unidades funcionais de forma coordenada.
Mudanças em determinadas condições, como o instante de contacto ou
impactos com o solo, levam a modelos com múltiplas fases. Assim, em vez de
forçar o movimento do robô a situações pré-determinadas de forma temporal, o
gerador de padrões de movimento proposto explora a transição entre diferentes
fases que surgem da interação do robô com o ambiente, despoletadas por
eventos sensoriais. A abordagem proposta é testada numa estrutura de
simulação dinâmica, sendo que várias experiências são efetuadas para avaliar
os métodos e o desempenho dos mesmos.This work addresses the problem of learning to imitate human locomotion actions
through low-level trajectories encoded with motion primitives and generalizing
them to new situations from a single demonstration. In this line of thought, the
main objectives of this work are twofold: The first is to analyze, extract and
encode human demonstrations taken from motion capture data in order to model
biped locomotion tasks. However, transferring motion skills from humans to
robots is not limited to the simple reproduction, but requires the evaluation of
their ability to adapt to new situations, as well as to deal with unexpected
disturbances. Therefore, the second objective is to develop and evaluate a
control framework for action shaping such that the single-demonstration can be
modulated to varying situations, taking into account the dynamics of the robot
and its environment.
The idea behind the approach is to address the problem of generalization from
a single-demonstration by combining two basic structures. The first structure is
a pattern generator system consisting of movement primitives learned and
modelled by dynamical systems (DS). This encoding approach possesses
desirable properties that make them well-suited for trajectory generation, namely
the possibility to change parameters online such as the amplitude and the
frequency of the limit cycle and the intrinsic robustness against small
perturbations. The second structure, which is embedded in the previous one,
consists of coupled phase oscillators that organize actions into functional
coordinated units. The changing contact conditions plus the associated impacts
with the ground lead to models with multiple phases. Instead of forcing the robot’s
motion into a predefined fixed timing, the proposed pattern generator explores
transition between phases that emerge from the interaction of the robot system
with the environment, triggered by sensor-driven events. The proposed approach
is tested in a dynamics simulation framework and several experiments are
conducted to validate the methods and to assess the performance of a humanoid
robot
The Runbot: engineering control applied to rehabilitation in spinal cord injury patients
Human walking is a complicated interaction among the musculoskeletal system, nervous
system and the environment. An injury affecting the neurological system, such as a spinal
cord injury (SCI) can cause sensor and motor deficits, and can result in a partial or complete
loss of their ambulatory functions. Functional electrical stimulation (FES), a technique to
generate artificial muscle contractions with the application of electrical current, has been
shown to improve the ambulatory ability of patients with an SCI. FES walking systems have
been used as a neural prosthesis to assist patients walking, but further work is needed to
establish a system with reduced engineering complexity which more closely resembles the
pattern of natural walking.
The aim of this thesis was to develop a new FES gait assistance system with a simple and
efficient FES control based on insights from robotic walking models, which can be used in
patients with neuromuscular dysfunction, for example in SCI.
The understanding of human walking is fundamental to develop suitable control strategies.
Limit cycle walkers are capable of walking with reduced mechanical complexity and simple
control. Walking robots based on this principle allow bio-inspired mechanisms to be analysed
and validated in a real environment. The Runbot is a bipedal walker which has been
developed based on models of reflexes in the human central nervous system, without the
need for a precise trajectory algorithm. Instead, the timing of the control pattern is based
on ground contact information. Taking the inspiration of bio-inspired robotic control, two
primary objectives were addressed. Firstly, the development of a new reflexive controller
with the addition of ankle control. Secondly, the development of a new FES walking system
with an FES control model derived from the principles of the robotic control system.
The control model of the original Runbot utilized a model of neuronal firing processes based
on the complexity of the central neural system. As a causal relationship between foot contact
information and muscle activity during human walking has been established, the control
model was simplified using filter functions that transfer the sensory inputs into motor outputs,
based on experimental observations in humans. The transfer functions were applied
to the RunBot II to generate a stable walking pattern. A control system for walking was
created, based on linear transfer functions and ground reaction information. The new control
system also includes ankle control, which has not been considered before. The controller
was validated in experiments with the new RunBot III.
The successful generation of stable walking with the implementation of the novel reflexive
robotic controller indicates that the control system has the potential to be used in controlling
the strategies in neural prosthesis for the retraining of an efficient and effective gait. To aid
of the development of the FES walking system, a reliable and practical gait phase detection
system was firstly developed to provide correct ground contact information and trigger timing
for the control. The reliability of the system was investigated in experiments with ten
able-bodied subjects. Secondly, an automatic FES walking system was implemented, which
can apply stimulation to eight muscles (four in each leg) in synchrony with the user’s walking
activity. The feasibility and effectiveness of this system for gait assistance was demonstrated
with an experiment in seven able-bodied participants.
This thesis addresses the feasibility and effectiveness of applying biomimetic robotic control
principles to FES control. The interaction among robotic control, biology and FES control
in assistive neural prosthesis provides a novel framework to developing an efficient and
effective control system that can be applied in various control applications