This thesis has been motivated by the growing needs for multi-degree of freedom (M-DOF) electromagnetic actuators capable of smooth and accurate multi-dimensional driving motions. Because high coercive rare-earth permanent-magnets (PMs) are widely available at low cost, their uses for developing compact, energy-efficient M-DOF actuators have been widely researched. To facilitate design analysis and optimization, this thesis research seeks to develop a general method based on distributed source models to characterize M-DOF PM-based actuators and optimize their designs to achieve high torque-to-weight performance with compact structures To achieve the above stated objective, a new method that is referred to here as distributed multi-level current (DMC) utilizes geometrically defined point sources has been developed to model electromagnetic components and phenomena, which include PMs, electromagnets (EMs), iron paths and induced eddy current. Unlike existing numerical methods (such as FEM, FDM, or MLM) which solve for the magnetic fields from Maxwell’s equations and boundary conditions, the DMC-based method develops closed-form solutions to the magnetic field and force problems on the basis of electromagnetic point currents in a multi-level structure while allowing trade-off between computational speed and accuracy. Since the multi-level currents can be directly defined at the geometrically decomposed volumes and surfaces of the components (such as electric conductors and magnetic materials) that make up of the electromagnetic system, the DMC model has been effectively incorporated in topology optimization to maximize the torque-to-weight ratio of an electromechanical actuator. To demonstrate the above advantages, the DMC optimization has been employed to optimize the several designs ranging from conventional single-axis actuators, 2-DOF linear-rotary motors to 3-DOF spherical motors. The DMC modeling method has been experimentally validated and compared against published data. While the DMC model offers an efficient means for the design analysis and optimization of electromechanical systems with improved computational accuracy and speed, it can be extended to a broad spectrum of emerging and creative applications involving electromagnetic systems.

The work presented in this thesis has two distinct parts: (i) development of a spinal motion measurement technique and (ii) incorporation of the spinal motion measurement with galvanic vestibular stimulation (GVS) technology, acting as a balance assist device hereafter referred to as a galvanic vestibular stimulation device (GVSD). The developed spinal motion measurement technique fulfills seven desired attributes: accuracy, portability, real-time data capture of dynamic data, non-invasive, small device footprint, clinically useful and of non-prohibitive cost. Applications of the proposed system range from diagnosis of spine injury to postural and balance monitoring, on-field as well as in the lab setting. The system is comprised of three inertial measurement sensors, respectively attached and calibrated to the head, torso and hips, based on the subject’s anatomical planes. Sensor output is transformed into meaningful clinical parameters of rotation, flexion-extension and lateral bending of each body segment with respect to a global reference space, then collected and visualized via an interactive graphical user interface (GUI). The accuracy of the proposed sensing system has been successfully verified with subject trials using a VICON optical motion measurement system. Next, the proposed motion measurement system and technique has been used to record a standing iv subject’s motion response to GVS. The data obtained allows the development of a new GVSD with the attributes of: eligibility for commercial licensing, portability, and capable of safely providing controlled stimulating current to the mastoid bones at varying levels and frequencies. The successful combination of the spinal motion measurement technique and GVSD represents the preliminary stage of a balance prosthesis.

Walking comes naturally to us and appears to be simple. However, this is not so and it is known that walking requires high level neural control and muscle coordination. There is no single, unifying theory of bipedal walking. Models of walking are useful in various ways such as developing computational theories of neural control, understanding muscle coordination and to design and analyze lower extremity prostheses. This thesis deals with modeling and simulation of walking from a kinematics and dynamics view point. Three sagittal planar models with increasing levels of complexity are presented in this thesis. The first model is a simple two degrees of freedom (DoF) model representing the motion at the hip and the knee joint. The second model is a three DoF model where the ankle joint motion is also taken into account. Finally, the third model considers both the legs and has seven DoF. The kinematic and dynamic equations of the models are derived, and the inverse dynamic analysis and forward dynamic simulation of the models are performed. The simulation results are compared with experimental data available in literature.

No description available.

This thesis project covers the development of a cross country skiing robot, with the purpose of being used as an instrument for measuring gliding properties of skis. The robot used in total 4 servomotors to control the motion of right and left arms with poles attached. A general movement pattern generator was developed to construct patterns that resemble the one of a human hand during poling. The final robot is able to generate a poling motion resulting in a forward propulsion, but further development to the design has to be made to use the robot for its intended purpose.

Yes / A 2D coupled temperature-displacement FE model is developed for a pad-disc brake system based on a restricted rotational pad boundary condition. The evolution of pressure, heat flux, and temperature along the contact interface during braking applications is analysed with the FE model. Results indicate that different rotational pad boundary conditions significantly impact the interface pressure distribution, which in turn affects interface temperature and heat flux distributions, and suggest that a particular pad rotation condition is most appropriate for accurately modelling friction braking processes. The importance of the thermal contact conductance in the analysis of heat transfer in friction braking is established, and it is confirmed that the heat partition ratio is not uniformly distributed along the interface under normal and high interface thermal conductance conditions.

The introduction of input devices with natural user interfaces in gaming hardware has changed the way we interact with games. Hardware with motion-sensing and gesture recognizing capabilities remove the constraint of interacting with games through typical traditional devices like mouse-keyboard and gamepads. This changes the way we approach games and how the game communicates back to us as the player opening new levels of interactivity. This thesis covers how eye tracker technology can be used to affect rendering effects in games.

The use of robotics has recently seen significant growth in various domains such as unmanned ground/underwater/aerial vehicles, smart manufacturing, and humanoid robots. However, one of the most important and essential capabilities required for long term autonomy, which is the ability to operate robustly and safely in real-world environments, in contrast to industrial and laboratory setup is largely missing. Designing robots that can operate reliably and efficiently in cluttered and changing environments is non-trivial, especially for high degree-of-freedom (DoF) systems, i.e. robots with multiple actuators. On one hand, the dexterity offered by the kinematic redundancy allows the robot to perform dexterous manipulation tasks in complex environments, whereas on the other hand, such complex system also makes controlling and planning very challenging. To address such two interrelated problems, we exploit robot motion synthesis from three perspectives that feed into each other: end-pose planning, motion planning and motion adaptation. We propose several novel ideas in each of the three phases, using which we can efficiently synthesise dexterous manipulation motion for fixed-base robotic arms, mobile manipulators, as well as humanoid robots in cluttered and potentially changing environments. Collision-free inverse kinematics (IK), or so-called end-pose planning, a key prerequisite for other modules such as motion planning, is an important and yet unsolved problem in robotics. Such information is often assumed given, or manually provided in practice, which significantly limiting high-level autonomy. In our research, by using novel data pre-processing and encoding techniques, we are able to efficiently search for collision-free end-poses in challenging scenarios in the presence of uneven terrains. After having found the end-poses, the motion planning module can proceed. Although motion planning has been claimed as well studied, we find that existing algorithms are still unreliable for robust and safe operations in real-world applications, especially when the environment is cluttered and changing. We propose a novel resolution complete motion planning algorithm, namely the Hierarchical Dynamic Roadmap, that is able to generate collision-free motion trajectories for redundant robotic arms in extremely complicated environments where other methods would fail. While planning for fixed-base robotic arms is relatively less challenging, we also investigate into efficient motion planning algorithms for high DoF (30 - 40) humanoid robots, where an extra balance constraint needs to be taken into account. The result shows that our method is able to efficiently generate collision-free whole-body trajectories for different humanoid robots in complex environments, where other methods would require a much longer planning time. Both end-pose and motion planning algorithms compute solutions in static environments, and assume the environments stay static during execution. While human and most animals are incredibly good at handling environmental changes, the state-of-the-art robotics technology is far from being able to achieve such an ability. To address this issue, we propose a novel state space representation, the Distance Mesh space, in which the robot is able to remap the pre-planned motion in real-time and adapt to environmental changes during execution. By utilizing the proposed end-pose planning, motion planning and motion adaptation techniques, we obtain a robotic framework that significantly improves the level of autonomy. The proposed methods have been validated on various state-of-the-art robot platforms, such as UR5 (6-DoF fixed-base robotic arm), KUKA LWR (7-DoF fixed-base robotic arm), Baxter (14-DoF fixed-base bi-manual manipulator), Husky with Dual UR5 (15-DoF mobile bi-manual manipulator), PR2 (20-DoF mobile bi-manual manipulator), NASA Valkyrie (38-DoF humanoid) and many others, showing that our methods are truly applicable to solve high dimensional motion planning for practical problems.

The aim of this work is to design a method for the object localization in the point could and as accurately as possible estimates the 6D pose of known objects in the industrial scene for bin picking. The design of the solution is inspired by the PoseCNN network. The solution also includes a scene simulator that generates artificial data. The simulator is used to generate a training data set containing 2 objects for training a convolutional neural network. The network is tested on annotated real scenes and achieves low success, only 23.8 % and 31.6 % success for estimating translation and rotation for one type of obejct and for another 12.4 % and 21.6 %, while the tolerance for correct estimation is 5 mm and 15°. However, by using the ICP algorithm on the estimated results, the success of the translation estimate is 81.5 % and the rotation is 51.8 % and for the second object 51.9 % and 48.7 %. The benefit of this work is the creation of a generator and testing the functionality of the network on small objects

<p dir="ltr">A Human-Machine Interaction (HMI) refers to a mechanism to support the direct interactions of humans and machines with the objective for the synthesis of machine intelligence and autonomy. The demand to advance in this field of study for intelligence controls is continuously growing. Brain-Computer Interface (BCI) is one type of HMIs that utilizes a human brain to enable direct communication of the human subject with a machine. This technology is widely explored in different fields to control external devices using brain signals.</p><p dir="ltr">This thesis is driven by two key observations. The first one is the limited number of Degrees of Freedom (DoF) that existing BCI controls can control in an external device; it becomes necessary to assess the controllability when choosing a control instrument. The second one is the differences of decision spaces of human and machine when both of them try to control an external device. To fill the gaps in these two aspects, there is a need to design an additional functional module that is able to translate the commands issued by human into high-frequency control commands that can be understood by machines. These two aspects has not been investigated thoroughly in literatures.</p><p dir="ltr">This study focuses on training, detecting, and using humans’ intents to control intelligent machines. It uses brain signals which will be trained and detected in form of Electroencephalography (EEG), brain signals will be used to extract and classify human intents. A selected instrument, Emotiv Epoc X, is used for pattern training and recognition based on its controllability and features among other instruments. A functional module is then developed to bridge the gap of frequency differences between human intents and motion commands of machine. A selected robot, TinkerKit Braccio, is then used to illustrate the feasibility of the developed module through fully controlling the robotic arm using human’s intents solely.</p><p dir="ltr">Multiple experiments were done on the prototyped system to prove the feasibility of the proposed model. The accuracy to send each command, and hence the accuracy of the system to extract each intent, exceeded 75%. Then, the feasibility of the proposed model was also tested through controlling the robot to follow pre-defined paths, which was obtained through designing a Graphical-User Interface (GUI). The accuracy of each experiment exceeded 90%, which validated the feasibility of the proposed control model.</p>