4 research outputs found
A review on humanoid robotics in healthcare
Humanoid robots have evolved over the years and today it is in many different areas of applications, from homecare to social care and healthcare robotics. This paper deals with a brief overview of the current and potential applications of humanoid robotics in healthcare settings. We present a comprehensive contextualization of humanoid robots in healthcare by identifying and characterizing active research activities on humanoid robot that can work interactively and effectively with humans so as to fill some identified gaps in current healthcare deficiency
UWOMJ Volume 82, No 2, Fall 2013
Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1068/thumbnail.jp
Otimização de locomoção bípede
Dissertação de mestrado integrado em Engenharia BiomédicaAtualmente verifica-se um crescimento exponencial a nível de desenvolvimento
de sistemas robóticos móveis havendo um esforço para criar sistemas com
propriedades mais eficientes e adaptáveis às exigências do ambiente de trabalho.
Neste contexto, têm havido uma preocupação acrescida em desenvolver melhores
sistemas de locomoção quer seja locomoção por rodas quer seja por pernas (bípede,
quadrúpede e hexapode).
Esta dissertação foca-se na otimização da locomoção bípede a qual é uma área
que tem sido alvo de grande atenção uma vez que esta é uma área da robótica que
ainda necessita de progredir no sentido de conseguir finalmente uma locomoção tão
eficiente como a marcha humana.
Deste modo, a elaboração deste trabalho teve como objetivos principais a
criação de uma estratégia de otimização que combinasse a geração de padrões de
movimento através de geradores centrais de padrões (CPGs) com um algoritmo de
otimização evolucionário (Non-Dominated Sorting Genetic Algorithm ll). Essa estratégia
implicou a determinação de objetivos que correspondem a características da
locomoção bípede e que foram otimizados, sendo eles o deslocamento frontal, a altura
a que o pé levanta, a força de impacto entre os pés e o chão e a posição do centro de
massa.
Os resultados foram obtidos a partir de simulações na plataforma Webots para
o robô bípede Darwin-OP. Neste contexto, os resultados foram muito satisfatórios uma
vez que o algoritmo foi capaz de gerar locomoção estável e os objetivos propostos
foram otimizados. Foi feito também um estudo de sensibilidade que determinou a
existência de parâmetros de CPGs que apresentam uma forte correlação positiva com
as funções objetivos. Assim, os parâmetros Acompasso, frequência ω e ORoll influenciam
fortemente o deslocamento e a força de impacto e o parâmetro AhPitch influencia a
altura a que o pé levanta.
No futuro seria pertinente aplicar o algoritmo elaborado num robô bípede real
e conferir se consegue gerar uma locomoção eficiente em condições reais.Presently there is an exponential increase on the level of development of
mobile robotic systems and so there is an effort to create systems with properties
more efficient and adaptable to the demands of the work environment. In this context,
there has been a heightened concern in developing better systems of locomotion
either by wheels either by legs (bipedal, 4-legged or 6-legged).
This dissertation focuses on the optimization of bipedal locomotion which is an
area that has been the subject of much attention since this is an area of robotics that
still needs to make progress towards finally achieving locomotion as efficient as the
human gait.
Thus, this work aimed to create an optimization strategy that combines the
generation of movement patterns through central pattern generators (CPGs) with an
evolutionary optimization algorithm (Non-Dominated Sorting Genetic Algorithm II).
This strategy involved the determination of objectives that correspond to
characteristics of bipedal locomotion and that have been optimized, namely the
frontal displacement, the ground clearance, the impact force between the foot and the
ground and the position of the center of mass.
The results were obtained from simulations in Webots platform for the bipedal
robot Darwin-OP. The results were very satisfactory since the algorithm was able to
generate stable locomotion and the proposed objectives were optimized. We also
made a sensitivity analysis that determined the existence of CPGs parameters that
exhibit a strong positive correlation with the objective functions. Thus, the parameters
Acompasso, the frequency ω and ORoll strongly influence the impact force and
displacement as well as AhPitch influences the height to which the foot rises.
In the future it would be appropriate to apply the developed algorithm in a real
biped robot and check if it can generate an efficient locomotion in real conditions
Facilitating Reliable Autonomy with Human-Robot Interaction
Autonomous robots are increasingly deployed to complex environments in which we cannot predict all possible failure cases a priori. Robustness to failures can be provided by humans enacting the roles of: (1) developers who can iteratively incorporate robustness into the robot system, (2) collocated bystanders who can be approached for aid, and (3) remote teleoperators who can be contacted for guidance.
However, assisting the robot in any of these roles can place demands on the time or effort of the human. This dissertation develops modules to reduce the frequency and duration of failure interventions in order to increase the reliability of autonomous robots, while also reducing the demand on humans. In pursuit of that goal, the dissertation makes the following contributions:
(1) A development paradigm for autonomous robots that separates task specification from error recovery. The paradigm reduces burden on developers while making the robot robust to failures.
(2) A model for gauging the interruptibility of collocated humans. A human-subjects study shows that using the model can reduce the time expended by the robot during failure recovery.
(3) A human-subjects experiment on the effects of decision support provided to remote operators during failures. The results show that humans need both diagnosis and action recommendations as decision support during an intervention.
(4) An evaluation of model features and unstructured Machine Learning (ML) techniques in pursuit of learning robust suggestions models from intervention data, in order to reduce developer effort. The results indicate that careful crafting of features can lead to improved performance, but that without such feature selection, current ML algorithms lack robustness in addressing a domain where the robot's observations are heavily influenced by the user's actions.Ph.D