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Integrating computer vision into a robotic system can provide a closed-loop controlled platform that increases the robustness of a robot's motion. This integration is also known as visual servo control or visual servoing. Visual servoing of a robot manipulator in real-time presents complex engineering problems with respect to both control and image processing particularly when we want the robot arm to perform complicated tasks such as portrait drawing. In my research, the implementation of torque feedback control and Image-based Visual Servoing (IBVS) approaches are proposed to improve previous open-loop portrait drawing tasks performed by Betty, a humanoid robot in the Autonomous Agent Lab, University of Manitoba. The implementations and evaluations of hardware, software and kinematic models are discussed in this document. I examined the problem of estimating ideal edges joining points in a pixel reduction image for an existing point-to-point portrait drawing humanoid robot, Betty. To solve this line drawing problem, two automatic sketch generators are presented. First, a modified Theta-graph, called Furthest Neighbour Theta-graph (FNTG). Second, an extension of the Edge Drawing Lines algorithm (EDLines), called Extended Edge Drawing Lines (eEDLines). The results show that the number of edges in the resulting drawing is significantly reduced without degrading the detail of the output image. The other main objective of this research is to propose the extension of the drawing robot project to further develop a robust visual servoing system for Betty to correct any drawing deviation in real-time as a human does. This is achieved by investigating and developing robust feature (lines and shading) extraction approaches for real-time feature tracking of IBVS in combination with adequate torque feedback in the drawing task.

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This paper discusses the design and implementation of a high performance, custom control solution for series elastic actuators (SEA) in a parallel or serial configuration. In many modern robotics applications, controlling actuator output force accurately and with high bandwidth is extremely important. The series elastic actuator has become popular in applications which require precise force control, however currently not many commercial options exist. Commonly, these actuators are custom designed and use electric motors, however most off-the-shelf electric motor drives are not designed for this specific application. In this paper, the hardware and software architecture of a control device designed specifically for force controlled series elastic actuators is described, along with test results on a novel SEA design. / Master of Science

In this thesis, we study the development of linguistic concepts (corresponding to a subset of nouns, verbs and adjectives) on a humanoid robot. To accomplish this goal, we use affordances, a notion first proposed by J.J. Gibson to describe the action possibilities offered to an agent by the environment. Using the affordances formalization framework of Sahin et al., we have implemented a learning system on a humanoid robot and obtained the required data from the sensorimotor experiences of the robot. The system we developed (1) can learn verb, adjective and noun concepts, (2) represent them in terms of strings of prototypes and dependencies based on affordances, (3) can accurately recognize the concept of novel objects and events, and (4) can be used for tasks such as goal emulation and multi step planning.

This dissertation presents the results of a series of studies on dynamic stabilisation of CoMan, which is actuated by series elastic actuators. The main goal of this dissertation is to dynamically stabilise the humanoid robot on the floor by the simplest multivariate feedback control for the purpose of walking. The multivariable scheme is chosen to take into account the joints' interactions, as well as providing a systematic way of designing the feedback system to improve the bandwidth and tracking performance of CoMan's existing PID control. A detailed model is derived which includes all the motors and joints state variables and their multibody interactions which are often ignored in the previous studies on bipedal robots in the literature. The derived dynamic model is then used to design multivariable optimal control feedback and observers with a mathematical proof for the relative stability and robustness of the closed loop system in face of model uncertainties and disturbances. In addition, two decentralized optimal feedback design algorithms are presented that explicitly take the compliant dynamics and the multibody interactions into account while providing the mathematical proof for the stability of the overall system. The purpose of the proposed decentralized control methods is to provide a systematic model based PDPID design to replace the existing PID controllers which are derived by a trial and error process. Moreover, the challenging constrained and compliant motion of the robot in double support is studied where a novel constrained feedback design is proposed which directly takes the compliance dynamics, interactions and the constraints into account to provide a closed loop feedback tracking system that drives the robot inside the constrained subspace. This method of control is particularly interesting since most control methods applied to closed kinematic chains (such as the double support phase) are over complicated for implementation purposes or have an ad-hoc approach to controller design. In terms of walking trajectory generation, an extension to the ZMP walking trajectory generation is proposed to utilise the CoMan's upper body to tackle the non-minimum phase behaviour that is faced in trajectory generation. Simple inverted pendulum models of walking are then used to study the maximum feasible walking speed and step size where parameters of CoMan are used to provide numerical upperbounds on the step size and walking speed. Use of straight knee and toe push-off during walking is shown to be beneficial for taking larger step lengths and hence achieving faster walking speeds. Subsequently, the designed tracking systems are then applied to a dynamic walking simulator which is developed during this PhD project to accurately model the compliant walking behaviour of the CoMan. A walking gait is simulated and visualized to show the effectiveness of the developed walking simulator. Moreover, the experimental results and challenges faced during the implementation of the designed tracking control systems are discussed where it is shown that the LQR feedback results in 50% less control effort and tracking errors in comparison with CoMan's existing independent PID control. This advantage directly affects the feasible walking speed. In addition, a set of standard and repeatable tests for CoMan are designed to quantify and compare the performance of various control system designs. Finally, the conclusions and future directions are pointed out.

Embedded systems for humanoid robots are required to be reliable, low in cost, scalable and robust. Most of the applications related to humanoid robots require efficient force control of Series Elastic Actuators (SEA). These control loops often introduce precise timing requirements due to the safety critical nature of the underlying hardware. Also the motor controller needs to run fast and interface with several sensors. The commercially available motor controllers generally do not satisfy all the requirements of speed, reliability, ease of use and small size. This work presents a custom motor controller, which can be used for real time force control of SEA on humanoid robots and exoskeletons. Emphasis has been laid on designing a system which is scalable, easy to use and robust. The hardware and software architecture for control has been presented along with the results obtained on a novel Series Elastic Actuator based humanoid robot THOR. / Master of Science / Humanoid robots can be used in several applications such as disaster management, replacing manual work in hazardous environments, helping human beings in navigation and day to day activities, etc. This increase in interests in humanoid robotics and related research in exoskeletons has led to the need of reliable embedded systems which is used to control the machines. These embedded systems are often required to be low in cost, scalable and robust. The specification required from the electronics and the embedded systems vary based on the robot’s capabilities. Also, there is a gap between the requirements of humanoid robots in research and in industrial setting. This work focuses on bridging the gap by proposing a solution which is semi-custom, low in cost, reliable and scalable. The work has been shown to perform as expected on the stat-of-art humanoid robot THOR which was built at Virginia Tech. Using the proposed design technique can not only deliver good performance but can also act as a quick prototyping tool for other robotics projects related to humanoid robotics and exoskeletons.

The works reported in this thesis focus upon synthesising neural controllers for anthropomorphic robots that are able to manipulate objects through an automatic design process based on artificial evolution. The use of Evolutionary Robotics makes it possible to reduce the characteristics and parameters specified by the designer to a minimum, and the robot’s skills evolve as it interacts with the environment. The primary objective of these experiments is to investigate whether neural controllers that are regulating the state of the motors on the basis of the current and previously experienced sensors (i.e. without relying on an inverse model) can enable the robots to solve such complex tasks. Another objective of these experiments is to investigate whether the Evolutionary Robotics approach can be successfully applied to scenarios that are significantly more complex than those to which it is typically applied (in terms of the complexity of the robot’s morphology, the size of the neural controller, and the complexity of the task). The obtained results indicate that skills such as reaching, grasping, and discriminating among objects can be accomplished without the need to learn precise inverse internal models of the arm/hand structure. This would also support the hypothesis that the human central nervous system (cns) does necessarily have internal models of the limbs (not excluding the fact that it might possess such models for other purposes), but can act by shifting the equilibrium points/cycles of the underlying musculoskeletal system. Consequently, the resulting controllers of such fundamental skills would be less complex. Thus, the learning of more complex behaviours will be easier to design because the underlying controller of the arm/hand structure is less complex. Moreover, the obtained results also show how evolved robots exploit sensory-motor coordination in order to accomplish their tasks.

Techniques of Artificial Intelligence and Human-Computer Interaction have empowered computer music systems with the ability to perform with humans via a wide spectrum of applications. However, musical interaction between humans and machines is still far less musical than the interaction between humans since most systems lack any representation or capability of musical expression. This thesis contributes various techniques, especially machine-learning algorithms, to create artificial musicians that perform expressively and collaboratively with humans. The current system focuses on three aspects of expression in human-computer collaborative performance: 1) expressive timing and dynamics, 2) basic improvisation techniques, and 3) facial and body gestures. Timing and dynamics are the two most fundamental aspects of musical expression and also the main focus of this thesis. We model the expression of different musicians as co-evolving time series. Based on this representation, we develop a set of algorithms, including a sophisticated spectral learning method, to discover regularities of expressive musical interaction from rehearsals. Given a learned model, an artificial performer generates its own musical expression by interacting with a human performer given a predefined score. The results show that, with a small number of rehearsals, we can successfully apply machine learning to generate more expressive and human-like collaborative performance than the baseline automatic accompaniment algorithm. This is the first application of spectral learning in the field of music. Besides expressive timing and dynamics, we consider some basic improvisation techniques where musicians have the freedom to interpret pitches and rhythms. We developed a model that trains a different set of parameters for each individual measure and focus on the prediction of the number of chords and the number of notes per chord. Given the model prediction, an improvised score is decoded using nearest-neighbor search, which selects the training example whose parameters are closest to the estimation. Our result shows that our model generates more musical, interactive, and natural collaborative improvisation than a reasonable baseline based on mean estimation. Although not conventionally considered to be “music,” body and facial movements are also important aspects of musical expression. We study body and facial expressions using a humanoid saxophonist robot. We contribute the first algorithm to enable a robot to perform an accompaniment for a musician and react to human performance with gestural and facial expression. The current system uses rule-based performance-motion mapping and separates robot motions into three groups: finger motions, body movements, and eyebrow movements. We also conduct the first subjective evaluation of the joint effect of automatic accompaniment and robot expression. Our result shows robot embodiment and expression enable more musical, interactive, and engaging human-computer collaborative performance.

The Humanoid Walking Robot (HWR) is a research platform for the study of legged and wearable robots actuated with Hydro Muscles. The fluid operated HWR is representative of a class of biologically inspired, and in some aspects highly biomimetic robotic musculoskeletal appendages showing certain advantages in comparison to more conventional artificial limbs and braces for physical therapy/rehabilitation, assistance of daily living, and augmentation. The HWR closely mimics the human body structure and function, including the skeleton, ligaments, tendons, and muscles. The HWR can emulate close to human-like movements even when subjected to simplified control laws. One of the main drawbacks of this approach is the inaccessibility of an appropriate fluid flow management support system, in the form of affordable, lightweight, compact, and good quality valves suitable for robotics applications. To resolve this shortcoming, the Compact Robotic Flow Control Valve (CRFC Valve) is introduced and successfully proof-of-concept tested. The HWR added with the CRFC Valve has potential to be a highly energy efficient, lightweight, controllable, affordable, and customizable solution that can resolve single muscle action.