Method and system for providing autonomous control of a platform

The present application provides a system for enabling instrument placement from distances on the order of five meters, for example, and increases accuracy of the instrument placement relative to visually-specified targets. The system provides precision control of a mobile base of a rover and onboard manipulators (e.g., robotic arms) relative to a visually-specified target using one or more sets of cameras. The system automatically compensates for wheel slippage and kinematic inaccuracy ensuring accurate placement (on the order of 2 mm, for example) of the instrument relative to the target. The system provides the ability for autonomous instrument placement by controlling both the base of the rover and the onboard manipulator using a single set of cameras. To extend the distance from which the placement can be completed to nearly five meters, target information may be transferred from navigation cameras (used for long-range) to front hazard cameras (used for positioning the manipulator)

Mobile camera-space manipulation

The invention is a method of using computer vision to control systems consisting of a combination of holonomic and nonholonomic degrees of freedom such as a wheeled rover equipped with a robotic arm, a forklift, and earth-moving equipment such as a backhoe or a front-loader. Using vision sensors mounted on the mobile system and the manipulator, the system establishes a relationship between the internal joint configuration of the holonomic degrees of freedom of the manipulator and the appearance of features on the manipulator in the reference frames of the vision sensors. Then, the system, perhaps with the assistance of an operator, identifies the locations of the target object in the reference frames of the vision sensors. Using this target information, along with the relationship described above, the system determines a suitable trajectory for the nonholonomic degrees of freedom of the base to follow towards the target object. The system also determines a suitable pose or series of poses for the holonomic degrees of freedom of the manipulator. With additional visual samples, the system automatically updates the trajectory and final pose of the manipulator so as to allow for greater precision in the overall final position of the system

“A very orderly retreat”: Democratic transition in East Germany, 1989-90

East Germany's 1989-90 democratisation is among the best known of East European transitions, but does not lend itself to comparative analysis, due to the singular way in which political reform and democratic consolidation were subsumed by Germany's unification process. Yet aspects of East Germany's democratisation have proved amenable to comparative approaches. This article reviews the comparative literature that refers to East Germany, and finds a schism between those who designate East Germany's transition “regime collapse” and others who contend that it exemplifies “transition through extrication”. It inquires into the merits of each position and finds in favour of the latter. Drawing on primary and secondary literature, as well as archival and interview sources, it portrays a communist elite that was, to a large extent, prepared to adapt to changing circumstances and capable of learning from “reference states” such as Poland. Although East Germany was the Soviet state in which the positions of existing elites were most threatened by democratic transition, here too a surprising number succeeded in maintaining their position while filing across the bridge to market society. A concluding section outlines the alchemy through which their bureaucratic power was transmuted into property and influence in the “new Germany”

The flux qubit revisited to enhance coherence and reproducibility

The scalable application of quantum information science will stand on reproducible and controllable high-coherence quantum bits (qubits). Here, we revisit the design and fabrication of the superconducting flux qubit, achieving a planar device with broad-frequency tunability, strong anharmonicity, high reproducibility and relaxation times in excess of 40 μs at its flux-insensitive point. Qubit relaxation times T₁ across 22 qubits are consistently matched with a single model involving resonator loss, ohmic charge noise and 1/f-flux noise, a noise source previously considered primarily in the context of dephasing. We furthermore demonstrate that qubit dephasing at the flux-insensitive point is dominated by residual thermal-photons in the readout resonator. The resulting photon shot noise is mitigated using a dynamical decoupling protocol, resulting in T₂≈85 μs, approximately the 2T₁ limit. In addition to realizing an improved flux qubit, our results uniquely identify photon shot noise as limiting T₂ in contemporary qubits based on transverse qubit–resonator interaction

Using obstacles and road pixels in the disparity-space computation of stereo-vision based occupancy grids

International audienceOccupancy grids have been used for a variety of applications in the field of robotics. These grids have typically been created based on data provided by range sensors such as laser or ultrasound. Current practice is to create the grids based on a probabilistic sensor model such as [1]. The use of stereo-vision to create occupancy grids is less common. This paper will detail a novel approach to compute occupancy grids, as applied to intelligent vehicles. Occupancy is initially computed directly in the stereoscopic sensor's disparity space, allowing the handling of occlusions in the observed area. It is also computationally efficient, since it uses the u-disparity approach to avoid processing a large point cloud. The occupancy calculation formally accounts for the detection of obstacles and the road in disparity space, as well as partial occlusions in the scene. In a second stage, this disparity-space occupancy grid is transformed into a Cartesian space occupancy grid to be used by subsequent applications. This transformation includes a filtering step to reduce discretization effects and explicitly account for the relation between range and uncertainty in stereoscopic data. In this paper, we present the method and show the results obtained with real road data

Determinants of Performance in Smallholder Farmer Groups in Uganda

The performance of farmer groups is critical for the success of the farmer-led Agricultural Extension approach currently used in Uganda. This study examines factors affecting performance of farmer groups accessing agricultural extension and advisory services from the National Agricultural Advisory Services in Eastern Uganda. The study collected data 200 members of 19 farmer groups in Eastern Uganda. Performance of farmer groups was the dependent variable, which was perceived to be influenced by individual members’ objectives, participation culture, power distance, structure of task, perceived equity, reward allocation and participation in group activities. Farmer group performance had a statistically significant positive relationship with power distance and perceived equity. Group participation culture and structure of tasks had a statistically negative relationship with group performance. Members tended to deflect group losses to factors beyond the seasonality of group activities, quality of farm inputs, and poor training delivered by advisory service providers. The advisory service providers and farmer group members need to use the political and social capital possessed by the local leadership, groups and community members for enhancing support and collective participation of the community in farmer groups. Since farmer groups are a sub-set of wider community, this empirical study brings into perspective the role of community culture in influencing performance of farmer groups in smallholder farming communities

Regional to Global Assessments of Phytoplankton Dynamics From The SeaWiFS Mission

Photosynthetic production of organic matter by microscopic oceanic phytoplankton fuels ocean ecosystems and contributes roughly half of the Earth's net primary production. For 13 years, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission provided the first consistent, synoptic observations of global ocean ecosystems. Changes in the surface chlorophyll concentration, the primary biological property retrieved from SeaWiFS, have traditionally been used as a metric for phytoplankton abundance and its distribution largely reflects patterns in vertical nutrient transport. On regional to global scales, chlorophyll concentrations covary with sea surface temperature (SST) because SST changes reflect light and nutrient conditions. However, the oceanmay be too complex to be well characterized using a single index such as the chlorophyll concentration. A semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chlorophyll a concentration, absorption by CDM and particulate backscattering. First, we show that observed long-term, global-scale trends in standard chlorophyll retrievals are likely compromised by coincident changes in CDM. Second, we partition the chlorophyll signal into a component due to phytoplankton biomass changes and a component caused by physiological adjustments in intracellular chlorophyll concentrations to changes in mixed layer light levels. We show that biomass changes dominate chlorophyll signals for the high latitude seas and where persistent vertical upwelling is known to occur, while physiological processes dominate chlorophyll variability over much of the tropical and subtropical oceans. The SeaWiFS data set demonstrates complexity in the interpretation of changes in regional to global phytoplankton distributions and illustrates limitations for the assessment of phytoplankton dynamics using chlorophyll retrievals alone

Using the disparity space to compute occupancy grids from stereo-vision

International audienceThe occupancy grid is a popular tool for probabilistic robotics, used for a variety of applications. Such grids are typically based on data from range sensors (e.g. laser, ultrasound), and the computation process is well known. The use of stereo-vision in this framework is less common, and typically treats the stereo sensor as a distance sensor, or fails to account for the uncertainties specific to vision. In this paper, we propose a novel approach to compute occupancy grids from stereo-vision, for the purpose of intelligent vehicles. Occupancy is initially computed directly in the stereoscopic sensor's disparity space, using the sensor's pixel-wise precision during the computation process and allowing the handling of occlusions in the observed area. It is also computationally efficient, since it uses the u-disparity approach to avoid processing a large point cloud. In a second stage, this disparity-space occupancy is transformed into a Cartesian space occupancy grid to be used by subsequent applications. In this paper, we present the method and show results obtained with real road data, comparing this approach with others