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In this paper we present a method for fusing optical flow and inertial measurements. To this end, we derive a novel visual error term which is better suited than the standard continuous epipolar constraint for extracting the information contained in the optical flow measurements. By means of an unscented Kalman filter (UKF), this information is then tightly coupled with inertial measurements in order to estimate the egomotion of the sensor setup. The individual visual landmark positions are not part of the filter state anymore. Thus, the dimensionality of the state space is significantly reduced, allowing for a fast online implementation. A nonlinear observability analysis is provided and supports the proposed method from a theoretical side. The filter is evaluated on real data together with ground truth from a motion capture system.
Posted on: July 16, 2014
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This paper introduces a method to simultaneously optimize design and control parameters for legged robots to improve the performance of locomotion based tasks. The morphology of a quadrupedal robot was optimized for a trotting and bounding gait to achieve a certain speed while tuning the control parameters of a robust locomotion controller at the same time. The results of the optimization show that a change of the structure of the robot can help increase its admissable top speed while using the same actuation units.
Posted on: July 16, 2014
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This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in prior work on a point-foot quadruped platform by adding the rotational constraints imposed by the humanoid’s flat feet. As in previous work, the proposed Extended Kalman Filter accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. A nonlinear observability analysis is performed on both the point-foot and flat-foot filters and it is concluded that the addition of rotational constraints significantly simplifies singular cases and improves the observability characteristics of the system. Results on a simulated walking dataset demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.
Posted on: July 16, 2014
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This paper presents a state estimation approach for legged robots based on stochastic filtering. The key idea is to extract information from the kinematic constraints given through the intermittent contacts with the ground and to fuse this information with inertial measurements. To this end, we design an unscented Kalman filter based on a consistent formulation of the underlying stochastic model. To increase the robustness of the filter, an outliers rejection methodology is included into the update step. Furthermore, we present the nonlinear observability analysis of the system, where, by considering the special nature of 3D rotations, we obtain a relatively simple form of the corresponding observability matrix. This yields, that, except for the global position and the yaw angle, all states are in general observable. This also holds if only one foot is in contact with the ground. The presented filter is evaluated on a real quadruped robot trotting over an uneven and slippery terrain.
Posted on: July 16, 2014
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This paper describes a hierarchical operational space control (OSC) method based on least square optimization and outlines different ways to reduce the dimensionality of the optimization vector. The framework allows to emulate various behaviors by prioritized task-space motion, joint torque, and contact force optimization. Moreover, a methodology is introduced to partially excite the natural dynamics of the robot by open-loop motor regulation while the entire behavior is stabilized by hierarchical OSC. As a major contribution, the presented control strategies are tested and validated in real hardware walking, trotting, and pronking experiments using a fully torque controllable quadrupedal robot.
Posted on: July 16, 2014
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A state-of-the-art simulation technique that solves the equations of motion together with the set-valued contact and impulse laws by the time-stepping scheme of Moreau is introduced to the legged robotics community. An analysis is given that shows which of the many variations of the method fits best to legged robots. Two different methods to solve the discretized normal cone inclusions are compared: the projected over-relaxed Jacobi and Gauss-Seidel iteration. The methods are evaluated for an electrically-driven quadrupedal robot in terms of robustness, accuracy, speed and ease of use. Furthermore, the dependence of the simulation speed on the choice of the generalized coordinates is examined. The proposed technique is implemented in C++ and compared to a fast and simple approach based on compliant contact models. In conclusion, the introduced method with hard contacts is very beneficial for the simulation of legged robots.
Posted on: July 14, 2014
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Developing control methods that allow legged robots to move with skill and agility remains one of the grand challenges in robotics. In order to achieve this ambitious goal, legged robots must possess a wide repertoire of motor skills. A scalable control architecture that can represent a variety of gaits in a unified manner is therefore desirable. Inspired by the motor learning principles observed in nature, we use an optimization approach to automatically discover and fine-tune parameters for agile gaits. The success of our approach is due to the controller parameterization we employ, which is compact yet flexible, therefore lending itself well to learning through repetition. We use our method to implement a flying trot, a bound and a pronking gait for StarlETH, a fully autonomous quadrupedal robot.
Posted on: July 14, 2014
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One of the challenges in using brain computer interfaces over extended periods of time is the uncertainty in the system. This uncertainty can be due to the user’s internal states, the non stationarity of the brain signals, or the variation of the class discriminative information over time. Therefore, the users are often unable to maintain the same accuracy and time efficiency in delivering BCI commands. In this paper, we tackle the issue of variation in BCI command delivery time for a motor imagery task with the aim of providing assistance through adaptive shared control. This is important mainly because having long delivery of mental commands leads to uncertainty in the user’s intent classification and limits the responsiveness of the system. In order to address this issue, we separate the trials into “long” and “short” groups so that we have the same number of trials in each group. We demonstrate that using only a few samples at the beginning of the trial, we are able to predict whether the current trial will be short or long with high accuracies (70% – 86%). Eventually, this prediction enables us to tune the shared control parameters to overcome the issue of uncertainty.
Posted on: July 11, 2014
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Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5ms before onset using the amplitude of on-going SCPs with above chance level performances between 875ms to 437.5ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5ms before onset of reach.
Posted on: July 7, 2014
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Calibration of brain-machine interfaces exploiting event-related potentials has to be performed for each experimental paradigm. Even if these signals have been used in previous experiments with different protocols. We show that use of signals from previous experiments can reduce the calibration time for single-trial classification of error-related potentials. Compensating latency variations across tasks yield up to a 50% reduction the training period in new experiments without decrease in online performance compared to the standard training.
Posted on: July 4, 2014
