21 research outputs found
Challenges in the Locomotion of Self-Reconfigurable Modular Robots
Self-Reconfigurable Modular Robots (SRMRs) are assemblies of autonomous robotic units, referred to as modules, joined together using active connection mechanisms. By changing the connectivity of these modules, SRMRs are able to deliberately change their own shape in order to adapt to new environmental circumstances. One of the main motivations for the development of SRMRs is that conventional robots are limited in their capabilities by their morphology. The promise of the field of self-reconfigurable modular robotics is to design robots that are robust, self-healing, versatile, multi-purpose, and inexpensive. Despite significant efforts by numerous research groups worldwide, the potential advantages of SRMRs have yet to be realized. A high number of degrees of freedom and connectors make SRMRs more versatile, but also more complex both in terms of mechanical design and control algorithms. Scalability issues affect these robots in terms of hardware, low-level control, and high-level planning. In this thesis we identify and target three major challenges: (i) Hardware design; (ii) Planning and control; and, (iii) Application challenges. To tackle the hardware challenges we redesigned and manufactured the Self-Reconfigurable Modular Robot Roombots to meet desired requirements and characteristics. We explored in detail and improved two major mechanical components of an SRMR: the actuation and the connection mechanisms. We also analyzed the use of compliant extensions to increase locomotion performance in terms of locomotion speed and power consumption. We contributed to the control challenge by developing new methods that allow an arbitrary SRMR structure to learn to locomote in an efficient way. We defined a novel bio-inspired locomotion-learning framework that allows the quick and reliable optimization of new gaits after a morphological change due to self-reconfiguration or human construction. In order to find new suitable application scenarios for SRMRs we envision the use of Roombots modules to create Self-Reconfigurable Robotic Furniture. As a first step towards this vision, we explored the use and control of Plug-n-Play Robotic Elements that can augment existing pieces of furniture and create new functionalities in a household to improve quality of life
Mechanical Intelligence Simplifies Control in Terrestrial Limbless Locomotion
Limbless locomotors, from microscopic worms to macroscopic snakes, traverse
complex, heterogeneous natural environments typically using undulatory body
wave propagation. Theoretical and robophysical models typically emphasize body
kinematics and active neural/electronic control. However, we contend that
because such approaches often neglect the role of passive, mechanically
controlled processes (those involving "mechanical intelligence"), they fail to
reproduce the performance of even the simplest organisms. To uncover principles
of how mechanical intelligence aids limbless locomotion in heterogeneous
terradynamic regimes, here we conduct a comparative study of locomotion in a
model of heterogeneous terrain (lattices of rigid posts). We used a model
biological system, the highly studied nematode worm Caenorhabditis elegans, and
a robophysical device whose bilateral actuator morphology models that of
limbless organisms across scales. The robot's kinematics quantitatively
reproduced the performance of the nematodes with purely open-loop control;
mechanical intelligence simplified control of obstacle navigation and
exploitation by reducing the need for active sensing and feedback. An active
behavior observed in C. elegans, undulatory wave reversal upon head collisions,
robustified locomotion via exploitation of the systems' mechanical
intelligence. Our study provides insights into how neurally simple limbless
organisms like nematodes can leverage mechanical intelligence via appropriately
tuned bilateral actuation to locomote in complex environments. These principles
likely apply to neurally more sophisticated organisms and also provide a design
and control paradigm for limbless robots for applications like search and
rescue and planetary exploration.Comment: Published in Science Robotic
Models for reinforcement learning and design of a soft robot inspired by Drosophila larvae
Designs for robots are often inspired by animals, as they are designed mimicking animals’
mechanics, motions, behaviours and learning. The Drosophila, known as the
fruit fly, is a well-studied model animal. In this thesis, the Drosophila larva is studied
and the results are applied to robots. More specifically: a part of the Drosophila larva’s
neural circuit for operant learning is modelled, based on which a synaptic plasticity
model and a neural circuit model for operant learning, as well as a dynamic neural network
for robot reinforcement learning, are developed; then Drosophila larva’s motor
system for locomotion is studied, and based on it a soft robot system is designed.
Operant learning is a concept similar to reinforcement learning in computer science,
i.e. learning by reward or punishment for behaviour. Experiments have shown
that a wide range of animals is capable of operant learning, including animal with only
a few neurons, such as Drosophila. The fact implies that operant learning can establish
without a large number of neurons. With it as an assumption, the structure and dynamics
of synapses are investigated, and a synaptic plasticity model is proposed. The
model includes nonlinear dynamics of synapses, especially receptor trafficking which
affects synaptic strength. Tests of this model show it can enable operant learning at the
neuron level and apply to a broad range of NNs, including feedforward, recurrent and
spiking NNs.
The mushroom body is a learning centre of the insect brain known and modelled
for associative learning, but not yet for operant learning. To investigate whether it participates
in operant learning, Drosophila larvae are studied with a transgenic tool by
my collaborators. Based on the experiment and the results, a mushroom body model
capable of operant learning is modelled. The proposed neural circuit model can reproduce
the operant learning of the turning behaviour of Drosophila larvae.
Then the synaptic plasticity model is simplified for robot learning. With the simplified
model, a recurrent neural network with internal neural dynamics can learn to
control a planar bipedal robot in a benchmark reinforcement learning task which is
called bipedal walker by OpenAI. Benefiting efficiency in parameter space exploration
instead of action space exploration, it is the first known solution to the task with reinforcement
learning approaches.
Although existing pneumatic soft robots can have multiple muscles embedded in
a component, it is far less than the muscles in the Drosophila larva, which are well-organised
in a tiny space. A soft robot system is developed based on the muscle pattern
of the Drosophila larva, to explore the possibility to embed a high density of muscles
in a limited space. Three versions of the body wall with pneumatic muscles mimicking
the muscle pattern are designed. A pneumatic control system and embedded control
system are also developed for controlling the robot. With a bioinspired body wall will
a large number of muscles, the robot performs lifelike motions in experiments
Bio-Inspired Robotics
Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field
Locomoção de humanoides robusta e versátil baseada em controlo analÃtico e fÃsica residual
Humanoid robots are made to resemble humans but their locomotion
abilities are far from ours in terms of agility and versatility. When humans
walk on complex terrains or face external disturbances, they
combine a set of strategies, unconsciously and efficiently, to regain
stability. This thesis tackles the problem of developing a robust omnidirectional
walking framework, which is able to generate versatile
and agile locomotion on complex terrains. We designed and developed
model-based and model-free walk engines and formulated the
controllers using different approaches including classical and optimal
control schemes and validated their performance through simulations
and experiments. These frameworks have hierarchical structures that
are composed of several layers. These layers are composed of several
modules that are connected together to fade the complexity and
increase the flexibility of the proposed frameworks. Additionally, they
can be easily and quickly deployed on different platforms.
Besides, we believe that using machine learning on top of analytical approaches
is a key to open doors for humanoid robots to step out of laboratories.
We proposed a tight coupling between analytical control and
deep reinforcement learning. We augmented our analytical controller
with reinforcement learning modules to learn how to regulate the walk
engine parameters (planners and controllers) adaptively and generate
residuals to adjust the robot’s target joint positions (residual physics).
The effectiveness of the proposed frameworks was demonstrated and
evaluated across a set of challenging simulation scenarios. The robot
was able to generalize what it learned in one scenario, by displaying
human-like locomotion skills in unforeseen circumstances, even in the
presence of noise and external pushes.Os robôs humanoides são feitos para se parecerem com humanos,
mas suas habilidades de locomoção estão longe das nossas em termos
de agilidade e versatilidade. Quando os humanos caminham em
terrenos complexos ou enfrentam distúrbios externos combinam diferentes
estratégias, de forma inconsciente e eficiente, para recuperar a
estabilidade. Esta tese aborda o problema de desenvolver um sistema
robusto para andar de forma omnidirecional, capaz de gerar uma locomoção
para robôs humanoides versátil e ágil em terrenos complexos.
Projetámos e desenvolvemos motores de locomoção sem modelos e
baseados em modelos. Formulámos os controladores usando diferentes
abordagens, incluindo esquemas de controlo clássicos e ideais,
e validámos o seu desempenho por meio de simulações e experiências
reais. Estes frameworks têm estruturas hierárquicas compostas por
várias camadas. Essas camadas são compostas por vários módulos
que são conectados entre si para diminuir a complexidade e aumentar
a flexibilidade dos frameworks propostos. Adicionalmente, o sistema
pode ser implementado em diferentes plataformas de forma fácil.
Acreditamos que o uso de aprendizagem automática sobre abordagens
analÃticas é a chave para abrir as portas para robôs humanoides
saÃrem dos laboratórios. Propusemos um forte acoplamento entre controlo
analÃtico e aprendizagem profunda por reforço. Expandimos o
nosso controlador analÃtico com módulos de aprendizagem por reforço
para aprender como regular os parâmetros do motor de caminhada
(planeadores e controladores) de forma adaptativa e gerar resÃduos
para ajustar as posições das juntas alvo do robô (fÃsica residual). A
eficácia das estruturas propostas foi demonstrada e avaliada em um
conjunto de cenários de simulação desafiadores. O robô foi capaz de
generalizar o que aprendeu em um cenário, exibindo habilidades de
locomoção humanas em circunstâncias imprevistas, mesmo na presença
de ruÃdo e impulsos externos.Programa Doutoral em Informátic
Opinions and Outlooks on Morphological Computation
Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others
Control of Bio-Inspired Sprawling Posture Quadruped Robots with an Actuated Spine
Sprawling posture robots are characterized by upper limb segments protruding horizontally from the body, resulting in lower body height and wider support on the ground. Combined with an actuated segmented spine and tail, such morphology resembles that of salamanders or crocodiles.
Although bio-inspired salamander-like robots with simple rotational limbs have been created, not much research has been done on kinematically redundant bio-mimetic robots that can closely replicate kinematics of sprawling animal gaits.
Being bio-mimetic could allow a robot to have some of the locomotion skills observed in those animals, expanding its potential applications in challenging scenarios. At the same time, the robot could be used to answer questions about the animal's locomotion.
This thesis is focused on developing locomotion controllers for such robots. Due to their high number of degrees of freedom (DoF), the control is based on solving the limb and spine inverse kinematics to properly coordinate different body parts. It is demonstrated how active use of a spine improves the robot's walking and turning performance. Further performance improvement across a variety of gaits is achieved by using model predictive control (MPC) methods to dictate the motion of the robot's center of mass (CoM).
The locomotion controller is reused on an another robot (OroBOT) with similar morphology, designed to mimic the kinematics of a fossil belonging to Orobates, an extinct early tetrapod. Being capable of generating different gaits and quantitatively measuring their characteristics, OroBOT was used to find the most probable way the animal moved. This is useful because understanding locomotion of extinct vertebrates helps to conceptualize major transitions in their evolution.
To tackle field applications, e.g. in disaster response missions, a new generation of field-oriented sprawling posture robots was built. The robustness of their initial crocodile-inspired design was tested in the animal's natural habitat (Uganda, Africa) and subsequently enhanced with additional sensors, cameras and computer. The improvements to the software framework involved a smartphone user interface visualizing the robot's state and camera feed to improve the ease of use for the operator.
Using force sensors, the locomotion controller is expanded with a set of reflex control modules. It is demonstrated how these modules improve the robot's performance on rough and unstructured terrain.
The robot's design and its low profile allow it to traverse low passages. To also tackle narrow passages like pipes, an unconventional crawling gait is explored. While using it, the robot lies on the ground and pushes against the pipe walls to move the body. To achieve such a task, several new control and estimation modules were developed.
By exploring these problems, this thesis illustrates fruitful interactions that can take place between robotics, biology and paleontology