A leg proprioception based 6 DOF odometry for statically stable walking robots

This article presents a 3D odometry algorithm for statically stable walking robots that only uses proprioceptive data delivered by joint angle and joint torque sensors embedded within the legs. The algorithm intrinsically handles each kind of emerging statically stable gait and is independent of predefined gait patterns. Additionally, the algorithm can be equally applied to stiff robots as well as to robots with compliant joints. Based on the proprioceptive information a 6 degrees of freedom (DOF) pose estimate is calculated in three steps. First, point clouds, represented by the foot positions with respect to the body frame at two consecutive time steps, are matched and provide a 6 DOF estimate for the relative body motion. The obtained relative motion estimates are summed up over time giving a 6 DOF pose estimate with respect to the start frame. Second, joint torque measurement based pitch and roll angle estimates are determined. Finally in a third step, these estimates are used to stabilize the orientation angles calculated in the first step by data fusion employing an error state Kalman filter. The algorithm is implemented and tested on the DLR Crawler, an actively compliant six-legged walking robot. For this specific robot, experimental data is provided and the performance is evaluated in flat terrain and on gravel, at different joint stiffness settings and for various emerging gaits. Based on this data, problems associated with the odometry of statically stable walking robots are identified and discussed. Further, some results for crossing slopes and edges in a complete 3D scenario are presented.     

The effects of uncertainty on plan success in a simulated maintenance robot domain

Autonomous robots must be designed to function in real-world domains. These domains are characterized by uncertainty, yet little work has been done to quantify the effects of uncertainty on goal satisfaction. Our research on simulated repair robots indicates that different types of uncertainty have fundamentally different relationships to plan success. This paper investigates the impacts of sensor inaccuracies, exogenous events and failures in both motion and gripper operations. Extensive simulations provide the basis for mathematical models of the relationship between these types of uncertainties and plan success. In addition, the impact of current heuristics such as ‘retry on failure’, and ‘tell me three times’ is investigated. Concluding that even low levels of uncertainty can significantly impact goal satisfaction in a stimulated repair robot domain and that repeated attempts provide significant improvement in plan success rates in the face of uncertainty.     

Cooperative search for fair nurse rosters

The development of decision support systems acceptable for nurse rostering practitioners still presents a daunting challenge. Building on an existing nurse rostering problem, a set of fairness-based objective functions recently introduced in the literature has been extended. To this end, a generic agent-based cooperative search framework utilising new mechanisms is described, aiming to combine the strengths of multiple metaheuristics. These different metaheuristics represent individual planners’ implicit procedures for improving rosters. The framework enables to explore different ways of assessing nurse rosters in terms of fairness objectives. Computational experiments have been conducted across a set of benchmark instances. The overall results indicate that the proposed cooperative search for fair nurse rosters outperforms each metaheuristic run individually.     

Reconfigurable handling systems as an enabler for large components in mass customized production

Manufacturing companies today are exposed to increasingly dynamic market requirements. Particularly in high-wage countries, there is also a clear trend toward standing out from global competitors by means of product differentiation. At the same time, however, prices have to remain competitive. As a consequence, many companies choose a mass customization strategy to reconcile individualized products with advantages of the economies of scale. So far, this aim is mainly reached in the consumer goods sector by flexible production systems with a high degree of automation. The approach presented here is capable of transferring the benefits of mass customization to large products with lower production quantities such as aircraft by means of a highly reconfigurable automated handling system. Instead of manual operations or specific jigs and fixtures for each component, the new system is characterized by supporting large work pieces at several distributed points by multiple robotic arms. The individual robots are simple in design and very lightweight so that they can be easily switched in order to reconfigure the assembly system for different tasks. The development does not only comprise hardware construction, but also new methods for programming, commissioning and control of cooperating handling devices in a modular and reconfigurable system layout.     

A Competence Portfolio for Professionals in the Creative Industries

Within the last decade, the role of the Creative Industries has grown to become an important part of the economic system. The increasing acceleration of new developments in media and ICT technologies greatly affected the Creative Industries' dynamic with a direct impact on the people working in this sector. Since only a few studies focus on competences needs, more or less isolated from the trends within the industry, we address the topic of individual competence shifts in the turbulent environment of the Creative Industries. We investigated the trends regarding competence shifts and their implications as well as the competences which are essential for creative professionals. We conducted a broad literature review as well as a qualitative study, which includes interviews and workshops with industry experts on trends within the Creative Industries and corresponding dimensions and demands for competences. We present four requirements that call for shifts in the education of competences. Based on the discussion of requirements, we present a competence portfolio for the Creative Industries along the dimensions of professional, methodological and personal‐social competences. The portfolio clearly indicates which competences should be taken into consideration for the development of curricula and study programmes in the education of creative professionals. A generalization of these findings suggests new challenges for companies relying on creative professionals.     

DART: a 3D model for simulating satellite images and studying surface radiation budget

DART (Discrete Anisotropic Radiative Transfer) is a radiative transfer model that simulates remotely acquired images. It was originally developed to work in the short wavelengths (0.3–3?µm) within 3D natural scenes that are represented as matrices of rectangular cells containing trees, shrubs, grass, soil, etc. DART was recently modified to extend its domain of application and to improve its accuracy. This paper summarizes the major features of DART and presents the changes that were implemented for improving its accuracy. Presently, this model works with natural and urban landscapes, on the whole optical domain (thermal infrared included) and with a multispectral approach that uses optical data bases from 0.3?µm up to 15?µm. It simulates radiative transfer in the whole ‘atmosphere–Earth’ system and it accounts for the instrumental transfer function. Three major changes allowed us to improve DART accuracy by a factor of three: more accurate simulation of single and multiple scattering, use of a scheme that oversamples DART cells and a better account of the direction of radiation that gives rise to multiple scattered radiation.     

Estimation of tropical forest structure from SPOT-5 satellite images

Predictions of tropical forest structure at the landscape level still present relatively high levels of uncertainty. In this study we explore the capabilities of high-resolution Satellite Pour l'Observation de la Terre (SPOT)-5 XS images to estimate basal area, tree volume and tree biomass of a tropical rainforest region in Chiapas, Mexico. SPOT-5 satellite images and forest inventory data from 87 sites were used to establish a multiple linear regression model. The 87 0.1-ha plots covered a wide range of forest structures, including mature forest, with values from 74.7 to 607.1 t ha^?1. Spectral bands, image transformations and texture variables were explored as independent variables of a multiple linear regression model. The R²s of the final models were 0.58 for basal area, 0.70 for canopy height, 0.73 for bole volume, and 0.71 for biomass. A leave-one-out cross-validation produced a root mean square. error (RMSE) of 5.02 m² ha^?1 (relative RMSE of 22.8%) for basal area; 3.22 m (16.1%) for canopy height; 69.08 m³ ha^?1 (30.7%) for timber volume, and 59.3 t ha^?1 (21.2%) for biomass. In particular, the texture variable ‘variance of near-infrared’ turned out to be an excellent predictor for forest structure variables.     

Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico

A number of methods to overcome the 2003 failure of the Landsat 7 Enhanced Thematic Mapper (ETM+) scan-line corrector (SLC) are compared in this article in a forest-monitoring application in the Yucatan Peninsula, Mexico. The objective of this comparison is to determine the best approach to accomplish SLC-off image gap-filling for the particular landscape in this region, and thereby provide continuity in the Landsat data sensor archive for forest-monitoring purposes. Four methods were tested: (1) local linear histogram matching (LLHM); (2) neighbourhood similar pixel interpolator (NSPI); (3) geostatistical neighbourhood similar pixel interpolator (GNSPI); and (4) weighted linear regression (WLR). All methods generated reasonable SLC-off gap-filling data that were visually consistent and could be employed in subsequent digital image analysis. Overall accuracy, kappa coefficients (κ), and quantity and allocation disagreement indices were used to evaluate unsupervised Iterative Self-Organizing Data Analysis (ISODATA) land-cover classification maps. In addition, Pearson correlation coefficients (r) and root mean squares of the error (RMSEs) were employed for estimates agreement with fractional land cover. The best results visually (overall accuracy > 85%, κ < 9%, quantity disagreement index < 5.5%, and allocation disagreement index < 12.5%) and statistically (r > 0.84 and RMSE < 7%) were obtained from the GNSPI method. These results suggest that the GNSPI method is suitable for routine use in reconstructing the imagery stack of Landsat ETM+ SLC-off gap-filled data for use in forest-monitoring applications in this type of heterogeneous landscape.     

Fine spatial resolution residential land-use data for small-area population mapping: a case study in Riyadh,Saudi Arabia

Rapid growth in the world’s urban population presents many challenges to planning and service provision. Conventional sources of population data often fail to provide spatially and temporally detailed information on changing urban populations. While downscaling methods have helped bridge this gap, use of fine spatial resolution data coupled with object-based image analysis (OBIA) methods is relatively novel, and few studies exist outside the western, developed world. This article presents a study in Riyadh, Saudi Arabia, in which population distribution estimates were obtained by downscaling using detailed residential land-use classes derived from the application of OBIA to fine spatial resolution remotely sensed imagery. To assess the utility of these data for population downscaling, three statistical regression models (using built area, residential built area, and detailed residential built area) and two dasymetric areal interpolation models (using residential built area and detailed residential built area) were applied to downscale the density of dwelling units, prior to estimating the population distribution through a simple transform. The research suggests that, for regression, the proportion of residential land use (Model 2) increased the accuracy over built area proportion (Model 1), and, in a multivariate extension, the proportions of six separate residential land-use classes (Model 3) increased the accuracy further, thereby demonstrating the value of the fine spatial resolution imagery. For example, the actual number of dwelling units was 7771 and the estimated numbers of dwelling units of Models 1 and 3 were 10,598 and 8759, respectively. Moreover, the root mean square error (RMSE) was 5.9 for Model 1 and 2.6 for Model 3. Additionally, six-class dasymetric mapping was evaluated in comparison to the conventional binary dasymetric mapping approach. The six-class dasymetric mapping approach was found to be slightly more accurate than binary dasymetric mapping.     

Correction of cirrus effects in Sentinel-2 type of imagery

Optical satellite images are often contaminated with cirrus clouds. Thin cirrus can be detected with a channel at 1.38 μm, and an established cirrus removal method exists for visible/near-infrared (VNIR) channels in atmospheric window regions, which was demonstrated with Moderate Resolution Imaging Spectrometer (MODIS) data. This contribution addresses open issues of cirrus correction for Sentinel-2 type of instruments, that is, future spaceborne sensors such as Sentinel-2 or similar instruments. These issues are (i) an extension of the existing technique to account for cirrus during the water vapour retrieval (channel at 0.94 μm) and surface reflectance calculation to avoid reflectance artefacts at 0.94 μm, (ii) a discussion of options concerning cirrus removal in the short-wave infrared (SWIR, channels at 1.6 and 2.2 μm) region and (iii) an analysis of channel parallax (view angle) requirements to achieve a high-quality cirrus removal.