This project aims to design and construct a 3D CAD model of a humanoid robot head; this means the mechanisms that simulate the motions of the neck, the eyes and the eyelids. The project was developed in collaboration with Cognition and Interaction Laboratory at the University of Skövde. From the literature review, it was found that most of the humanoid robots at the market are able to perform neck movements. The problem is that the neck motions today are not smooth as human neck and the movements of face details, such as the eyes and the mouth, are less developed. Only robots created for interaction research between human and robots allows for face expressions. However, the rest of the bodies of such robots are not as well developed as the face. The conclusion is that there is no humanoid robot that presents a full expression face and a well-developed body. This project presents new mechanical concepts for how to provide smooth humanoid neck motions as well as how to show expressions of the robots face. Three parts of the humanoid heads: the neck, the eyes and the eyelids were investigated. By examining different mechanical concepts used today two types of mechanisms were found: parallel and serial. In the neck the serial mechanism was chosen because the motion obtained is smoother. The eyes and the eyelids were designed with a serial mechanism due to the limitations of the space in the head. The three parts were built in to a 3D CAD program in order to test the entire head mechanism. This results in a head mechanism that enables smooth motion of the neck and provides enough degrees of freedom to simulate feelings due to eye expressions.

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The objective of the thesis is utilizing body sensing technology to develop a more intuitive and convenient way to control robots. The idea is to build a body sensing control system based on Kinect framework. Through Kinect, users from different age groups can achieve the desired purposes through motion demonstration without complicated programming. The system can accurately calculate angle change from users¡¦ gestures in a motion and identify key-postures which can compose an emulation motion similar to the presenting one. In other words, from analyzing these key postures, the demonstrated behaviors are able to be represented internally. Therefore, the system, consisting of a kinematics computational module and a representation algorithm, not only provides the function of behavior emulation but also behavior representation. By representation algorithm, the system extracts the features of combined behaviors. Besides, with the modular programming methodology, different behaviors can be reorganized to generate new behaviors based on the set of key poses represented by the extracted features. The application implemented in this system is within the OpenNI and NITE environment. OpenNI is used to retrieve information that the Kinect captured. NITE is used to track the user skeleton. The system is demonstrated by a play of ¡§Tai-Ji-Advancer¡¨ and at http://www.youtube.com/watch?v=cSYS49JKVAA.

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.

No description available.

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 thesis focuses on implementing a program, using Python and the symbolic package SymPy, to evaluate balancing of a humanoid robot modelled as inverted pendulums. The balancing algorithm used to evaluate the program is the feedback controller LQR. The program has successfully implemented a working LQR algorithm together with features such as underactuation and a tilting plane as disturbance. We have shown that the energy is conserved for the falling pendulums and that it is possible to predict the behavior for certain parameter values of the pendulums, thus confirming that the program is working correctly. Furthermore we have shown that a fully-actuated system is more controllable than an under-actuated system, and for each actuator that is removed, the system becomes less controllable. Finally we discuss the program performance where some concern is given toward the seemingly poor execution time of the program. The program has been tested for up to five pendulums with successful results. Most of the results however, are revolving around three pendulum systems.

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