In the past several years, IMU's have been widely used to measure the orientation of a moving body over a continuous period of time. Although, inertial navigation is a common approach for estimating the orientation, it greatly suffers from the accumulation of error in the orientation estimation. Most of the current common practices apply zero velocity update as a calibration method to address this problem and improve the estimation accuracy. However, this approach requires the sensors to be stationary frequently. This thesis introduces a novel method of calibration for estimating the elevation and bank angles of the orientation over a persistent human movement utilizing accelerometers and gyroscopes. The proposed technique incorporates the prior knowledge about the human motion to the estimation of the orientation to prevent the estimated position from growing unboundedly. The measurement model is designed to estimate the position for T seconds in the future. The knowledge of the estimated position for few seconds further in the future provides a feedback for orientation estimation during the periods of time when the accelerometer's readings are significantly deviated from gravity. This work evaluates the performance of the proposed method in two different ways: 1. a model of human movement is designed to generate synthetic data which resembles human motion. 2. an experimental design is implemented using a robot arm and an actual IMU to capture real data. The performance of the new technique is compared with the results from the inertial navigation approach. It is demonstrated that the new method significantly improves the accuracy of the orientation estimation.

Thesis (Ph. D.)--University of California, Riverside, 2009. / Includes abstract. Title from first page of PDF file (viewed January 28, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 100-106).

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Thesis (MSc) -- University of Stellenbosch, 2003. / ENGLISH ABSTRACT: A need for intelligent robotic machines is identified. Research and experiments have focussed on stable, or relatively stable, dynamic simulated systems to demonstrate the feasibility of embedding advanced AI into dynamic physical systems. This thesis presents an attempt to scale the techniques to a dynamically highly unstable system - the coordination of movements in a humanoid model. Environmental simulation, articulated systems and artificial intelligence methods are identified as three essential layers for a complete and unified approach to embedding AI into robotic machinery. The history of the physics subsystem for this project is discussed, leading to the adoption of the Open Dynamics Engine as the physics simulator of choice. An approach to articulated systems is presented along with the EBNF of a hierarchical articulated system that was used to describe the model. A revised form of evolution is presented and justified. An AI model that makes use of this new evolutionary paradigm is introduced. A variety of AI variants are defined and simulated. The results of these simulations are presented and analysed. Based on these results recommendations for future work are made. / AFRIKAANSE OPSOMMING: Die beheer van dinamiese masjiene, soos intelligente robotte, is tans beperk tot fisies stabilie - of relatief stabiele - sisteme. In hierdie tesis word die tegnieke van kunsmatige intelligensie (KI) toegepas op die kontrole en beheer van 'n dinamies hoogs onstabiele sisteem: 'n Humanoïede model. Fisiese simulasie, geartikuleerde sisteme en kunmatige intelligensie metodes word geïdentifiseer as drie noodsaaklike vereistes vir 'n volledige en eenvormige benadering tot KI beheer in robotte. Die implementasie van 'n fisiese simulator word beskryf, en 'n motivering vir die gebruik van die sogenaamde "Open Dynamics Engine" as fisiese simulator word gegee. 'n Benadering tot geartikuleerde sisteme word beskryf, tesame met die EBNF van 'n hierargiese geartikuleerde sisteem wat gebruik is om die model te beskryf. 'n Nuwe interpretasie vir evolusie word voorgestel, wat die basis vorm van 'n KI model wat in die tesis gebruik word. 'n Verskeidenheid van KI variasies word gedefineer en gesimuleer, en die resultate word beskryf en ontleed. Voorstelle vir verdere navorsing word gemaak.

The need to characterize normal and pathological human movement has consistently driven researchers to develop new tracking devices and to improve movement analysis systems. Movement has traditionally been captured by either optical, magnetic, mechanical, structured light, or acoustic systems. All of these systems have inherent limitations. Optical systems are costly, require fixed cameras in a controlled environment, and suffer from problems of occlusion. Similarly, acoustic and structured light systems suffer from the occlusion problem. Magnetic and radio frequency systems suffer from electromagnetic disturbances, noise and multipath problems. Mechanical systems have physical constraints that limit the natural body movement. Recently, the availability of low-cost wearable inertial sensors containing accelerometers, gyroscopes, and magnetometers has provided an alternative means to overcome the limitations of other motion capture systems. Inertial sensors can be used to track human movement in and outside of a laboratory, cannot be occluded, and are low cost. To calculate changes in orientation, researchers often integrate the angular velocity. However, a relatively small error or drift in the measured angular velocity leads to large integration errors. This restricts the time of accurate measurement and tracking to a few seconds. To compensate that drift, complementary data from accelerometers and magnetometers are normally integrated in tracking systems that utilize the Kalman filter (KF) or the extended Kalman filter (EKF) to fuse the nonlinear inertial data. Orientation estimates are only accurate for brief moments when the body is not moving and acceleration is only due to gravity. Moreover, success of using magnetometers to compensate drift about the vertical axis is limited by magnetic field disturbance. We combine kinematic models designed for control of robotic arms with state space methods to estimate angles of the human shoulder and elbow using two wireless wearable inertial measurement units. The same method can be used to track movement of other joints using a minimal sensor configuration with one sensor on each segment. Each limb is modeled as one kinematic chain. Velocity and acceleration are recursively tracked and propagated from one limb segment to another using Newton-Euler equations implemented in state space form. To mitigate the effect of sensor drift on the tracking accuracy, our system incorporates natural physical constraints on the range of motion for each joint, models gyroscope and accelerometer random drift, and uses zero-velocity updates. The combined effect of imposing physical constraints on state estimates and modeling the sensor random drift results in superior joint angles estimates. The tracker utilizes the unscented Kalman filter (UKF) which is an improvement to the EKF. This removes the need for linearization of the system equations which introduces tracking errors. We validate the performance of the inertial tracking system over long durations of slow, normal, and fast movements. Joint angles obtained from our inertial tracker are compared to those obtained from an optical tracking system and a high-precision industrial robot arm. Results show an excellent agreement between joint angles estimated by the inertial tracker and those obtained from the two reference systems.

Indiana University-Purdue University Indianapolis (IUPUI) / Human-looking computer interfaces, including humanoid robots and animated humans, may elicit in their users eerie feelings. This effect, often called the uncanny valley, emphasizes our heightened ability to distinguish between the human and merely humanlike using both perceptual and cognitive approaches. Although reactions to uncanny characters are captured more accurately with emotional descriptors (e.g., eerie and creepy) than with cognitive descriptors (e.g., strange), and although previous studies suggest the psychological processes underlying the uncanny valley are more perceptual and emotional than cognitive, the deep roots of the concept of humanness imply the application of category boundaries and cognitive dissonance in distinguishing among robots, androids, and humans. First, laddering interviews (N = 30) revealed firm boundaries among participants’ concepts of animated, robotic, and human. Participants associated human traits like soul, imperfect, or intended exclusively with humans, and they simultaneously devalued the autonomous accomplishments of robots (e.g., simple task, limited ability, or controlled). Jerky movement and humanlike appearance were associated with robots, even though the presented robotic stimuli were humanlike. The facial expressions perceived in robots as improper were perceived in animated characters as mismatched. Second, association model testing indicated that the independent evaluation based on the developed indices is a viable quantitative technique for the laddering interview. Third, from the interviews several candidate items for the eeriness index were validated in a large representative survey (N = 1,311). The improved eeriness index is nearly orthogonal to perceived humanness (r = .04). The improved indices facilitate plotting relations among rated characters of varying human likeness, enhancing perspectives on humanlike robot design and animation creation.