Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms

This paper proposes a computationally efficient method to estimate the time-varying relative pose between two visual-inertial sensor rigs mounted on the flexible wings of a fixed-wing unmanned aerial vehicle (UAV). The estimated relative poses are used to generate highly accurate depth maps in real-time and can be employed for obstacle avoidance in low-altitude flights or landing maneuvers. The approach is structured as follows: Initially, a wing model is identified by fitting a probability density function to measured deviations from the nominal relative baseline transformation. At run-time, the prior knowledge about the wing model is fused in an Extended Kalman filter~(EKF) together with relative pose measurements obtained from solving a relative perspective N-point problem (PNP), and the linear accelerations and angular velocities measured by the two inertial measurement units (IMU) which are rigidly attached to the cameras. Results obtained from extensive synthetic experiments demonstrate that our proposed framework is able to estimate highly accurate baseline transformations and depth maps.Comment: Accepted for publication in IEEE International Conference on Robotics and Automation (ICRA), 2018, Brisban

Scenario-based optimization of merchant vessels

This thesis introduces a scenario approach for determining a vessel's future operational profile regarding speed, draught and sea state conditions. It follows the Monte-Carlo principle and features the multiple simulation of a vessel's service life on the basis of a designated trade description, stochastically developing economic and market conditions and environmental influences. Additionally, uncertainties and disruptive events (crises) are considered. The resulting operational profile provides the basis for a multi-objective hull form optimization leading to a robust, holistic ship design

Towards the Efficient Probabilistic Characterization of Tropical Cyclone-Generated Storm Surge Hazards Under Stationary and Nonstationary Conditions

The scarcity of observations at any single location confounds the probabilistic characterization of tropical cyclone-generated storm surge hazards using annual maxima and peaks-over-threshold methods. The EST and the JPM are indirect approaches aimed at estimating the probability distribution of the response variable of interest (i.e. storm surge) using the probability distributions of predictor variables (e.g. storm size, storm intensity etc.). In the first part of this work, the relative performance of the empirical simulation technique (EST; Borgman et al., 1992) and the joint probability method (JPM; Myers, 1970) is evaluated via stochastic simulation methods. It is shown that the JPM has greater predictive capability for the estimation of the frequency of tropical cyclone winds, an efficient proxy for storm surge. The traditional attractions of the EST have been its economy and ease of implementation; more efficient numerical approximation schemes such as Bayesian quadrature now exist, which allows for more cost effective implementation of the JPM. In addition, typical enhancements of the original EST approach, such as the introduction of synthetic storms to complement the historical sample, are largely ineffective. These observations indicate that the EST should no longer be considered a practical approach for the robust and reliable estimation of the exceedance probabilities of storm surge levels, as required for actuarial purposes, engineering design and flood risk management in tropical cyclone-prone regions. The JPM is, however, not applicable to extratropical storm-prone regions and nonstationary phenomena. Additionally, the JPM requires the evaluation of a multidimensional integral composed of the product of marginal and conditional probability distributions of storm descriptors. This integral is typically approximated as a weighted summation of discrete function evaluations in each dimension and extended to D-dimensions by tensor product rules. To adequately capture the dynamics of the underlying physical process—storm surge driven by tropical cyclone wind fields—one must maintain a large number of explanatory variables in the integral. The complexity and cost of the joint probability problem, however, increases exponentially with dimension, precluding the inclusion of more than a few (≤4) stochastic variables. In the second part of the work, we extend stochastic simulation approaches to the classical joint probability problem. The successful implementation of stochastic simulation to the storm surge frequency problem requires the introduction of a new paradigm: the use of a regression function constructed by the careful selection of an optimal training set from the storm sample space such that the growth of support nodes required for efficient interpolation remains nonexponential while preserving the performance of a product grid equivalent. Apart from retaining the predictive capability of the JPM, the stochastic simulation approach also allows for nonstationary phenomena such as the effects of climate change on tropical cyclone activity to be efficiently modeled. A great utility of the stochastic approach is that the random sampling scheme is readily modified so that it conducts empirical simulation if required in place of parametric simulation. The enhanced empirical simulation technique attains predictive capabilities that are comparable with the JPM and the parametric simulation approach, while also retaining the suitability of empirical methods for application to situations that confound parametric methods, such as, application to extratropical cyclones and complexly distributed data. The parametric and empirical simulation techniques, together, will enable seamless flood hazard estimation for the entire coastline of the United States, with simple elaborations where needed to allow for the joint occurrence of both tropical and extratropical storms as compound stochastic processes. The stochastic approaches proposed hold great promise for the efficient probabilistic modeling of other multi-parameter systems such as earthquakes and riverine floods

Real-Time Multi-Fisheye Camera Self-Localization and Egomotion Estimation in Complex Indoor Environments

In this work a real-time capable multi-fisheye camera self-localization and egomotion estimation framework is developed. The thesis covers all aspects ranging from omnidirectional camera calibration to the development of a complete multi-fisheye camera SLAM system based on a generic multi-camera bundle adjustment method

A comparison of statistical models for short categorical or ordinal time series with applications in ecology

We study two statistical models for short-length categorical (or ordinal) time series. The first one is a regression model based on generalized linear model. The second one is a parametrized Markovian model, particularizing the discrete autoregressive model to the case of categorical data. These models are used to analyze two data-sets: annual larch cone production and weekly planktonic abundance.Comment: 18 page