194 research outputs found
Development of an Improved Rotational Orthosis for Walking With Arm Swing and Active Ankle Control
Based on interlimb neural coupling, gait robotic systems should produce walking-like movement in both upper and lower limbs for effective walking restoration. Two orthoses were previously designed in our lab to provide passive walking with arm swing. However, an active system for walking with arm swing is desirable to serve as a testbed for investigation of interlimb neural coupling in response to voluntary input. Given the important function of the ankle joint during normal walking, this work aimed to develop an improved rotational orthosis for walking with arm swing, which is called ROWAS II, and especially to develop and evaluate the algorithms for active ankle control. After description of the mechanical structure and control schemes of the overall ROWAS II system, the closed-loop position control and adjustable admittance control algorithms were firstly deduced, then simulated in Matlab/Simulink and finally implemented in the ROWAS II system. Six able-bodied participants were recruited to use the ROWAS II system in passive mode, and then to estimate the active ankle mechanism. It was showed that the closed-loop position control algorithms enabled the ROWAS II system to track the target arm-leg walking movement patterns well in passive mode, with the tracking error of each joint <0.7°. The adjustable admittance control algorithms enabled the participants to voluntarily adjust the ankle movement by exerting various active force. Higher admittance gains enabled the participants to more easily adjust the movement trajectory of the ankle mechanism. The ROWAS II system is technically feasible to produce walking-like movement in the bilateral upper and lower limbs in passive mode, and the ankle mechanism has technical potential to provide various active ankle training during gait rehabilitation. This novel ROWAS II system can serve as a testbed for further investigation of interlimb neural coupling in response to voluntary ankle movement and is technically feasible to provide a new training paradigm of walking with arm swing and active ankle control
Automation and Robotics: Latest Achievements, Challenges and Prospects
This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections
Sliding Mode Control
The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area
Bio-Inspired Soft Artificial Muscles for Robotic and Healthcare Applications
Soft robotics and soft artificial muscles have emerged as prolific research areas and have gained substantial traction over the last two decades. There is a large paradigm shift of research interests in soft artificial muscles for robotic and medical applications due to their soft, flexible and compliant characteristics compared to rigid actuators. Soft artificial muscles provide safe human-machine interaction, thus promoting their implementation in medical fields such as wearable assistive devices, haptic devices, soft surgical instruments and cardiac compression devices. Depending on the structure and material composition, soft artificial muscles can be controlled with various excitation sources, including electricity, magnetic fields, temperature and pressure.
Pressure-driven artificial muscles are among the most popular soft actuators due to their fast response, high exertion force and energy efficiency. Although significant progress has been made, challenges remain for a new type of artificial muscle that is easy to manufacture, flexible, multifunctional and has a high length-to-diameter ratio. Inspired by human muscles, this thesis proposes a soft, scalable, flexible, multifunctional, responsive, and high aspect ratio hydraulic filament artificial muscle (HFAM) for robotic and medical applications. The HFAM consists of a silicone tube inserted inside a coil spring, which expands longitudinally when receiving positive hydraulic pressure. This simple fabrication method enables low-cost and mass production of a wide range of product sizes and materials. This thesis investigates the characteristics of the proposed HFAM and two implementations, as a wearable soft robotic glove to aid in grasping objects, and as a smart surgical suture for perforation closure. Multiple HFAMs are also combined by twisting and braiding techniques to enhance their performance.
In addition, smart textiles are created from HFAMs using traditional knitting and weaving techniques for shape-programmable structures, shape-morphing soft robots and smart compression devices for massage therapy. Finally, a proof-of-concept robotic cardiac compression device is developed by arranging HFAMs in a special configuration to assist in heart failure treatment.
Overall this fundamental work contributes to the development of soft artificial muscle technologies and paves the way for future comprehensive studies to develop HFAMs for specific medical and robotic requirements
New Approaches in Automation and Robotics
The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book
Innovative robot hand designs of reduced complexity for dexterous manipulation
This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects.
Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension.
Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty.
In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness.
Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm.
In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces
Stochastic optimal control with learned dynamics models
The motor control of anthropomorphic robotic systems is a challenging computational
task mainly because of the high levels of redundancies such systems exhibit. Optimality
principles provide a general strategy to resolve such redundancies in a task driven
fashion. In particular closed loop optimisation, i.e., optimal feedback control (OFC),
has served as a successful motor control model as it unifies important concepts such
as costs, noise, sensory feedback and internal models into a coherent mathematical
framework.
Realising OFC on realistic anthropomorphic systems however is non-trivial: Firstly,
such systems have typically large dimensionality and nonlinear dynamics, in which
case the optimisation problem becomes computationally intractable. Approximative
methods, like the iterative linear quadratic gaussian (ILQG), have been proposed to
avoid this, however the transfer of solutions from idealised simulations to real hardware
systems has proved to be challenging. Secondly, OFC relies on an accurate description
of the system dynamics, which for many realistic control systems may be unknown,
difficult to estimate, or subject to frequent systematic changes. Thirdly, many (especially
biologically inspired) systems suffer from significant state or control dependent
sources of noise, which are difficult to model in a generally valid fashion. This thesis
addresses these issues with the aim to realise efficient OFC for anthropomorphic
manipulators.
First we investigate the implementation of OFC laws on anthropomorphic hardware.
Using ILQG we optimally control a high-dimensional anthropomorphic manipulator
without having to specify an explicit inverse kinematics, inverse dynamics
or feedback control law. We achieve this by introducing a novel cost function that
accounts for the physical constraints of the robot and a dynamics formulation that resolves
discontinuities in the dynamics. The experimental hardware results reveal the
benefits of OFC over traditional (open loop) optimal controllers in terms of energy
efficiency and compliance, properties that are crucial for the control of modern anthropomorphic
manipulators.
We then propose a new framework of OFC with learned dynamics (OFC-LD) that,
unlike classic approaches, does not rely on analytic dynamics functions but rather updates
the internal dynamics model continuously from sensorimotor plant feedback. We
demonstrate how this approach can compensate for unknown dynamics and for complex
dynamic perturbations in an online fashion.
A specific advantage of a learned dynamics model is that it contains the stochastic
information (i.e., noise) from the plant data, which corresponds to the uncertainty in
the system. Consequently one can exploit this information within OFC-LD in order
to produce control laws that minimise the uncertainty in the system. In the domain of
antagonistically actuated systems this approach leads to improved motor performance,
which is achieved by co-contracting antagonistic actuators in order to reduce the negative
effects of the noise. Most importantly the shape and source of the noise is unknown
a priory and is solely learned from plant data. The model is successfully tested on an
antagonistic series elastic actuator (SEA) that we have built for this purpose.
The proposed OFC-LD model is not only applicable to robotic systems but also
proves to be very useful in the modelling of biological motor control phenomena and
we show how our model can be used to predict a wide range of human impedance
control patterns during both, stationary and adaptation tasks
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