Search Results

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.

The demand for higher strength, lighter gauge materials by automotive designers, requires superior quality high strength steels from the manufacturer. The modern hot strip mill for supplying these sheet and strip products has developed into a high capacity sophisticated metallurgical tool. Subtle changes in chemistry, temperature control or the rolling process can produce a variety of strengths, toughness or formability characteristics in the final product. The Coilbox is an innovation in hot strip mill equipment that was developed at Stelco for the purpose of overcoming a fundamental hot rolling problem of heat loss from the product during the rolling process. This paper explains the difference between conventional and Coilbox methods of rolling. The benefits of improved surface, gauge control, shape control and metallurgical uniformity are also discussed which can result in a lower cost product for the original equipment manufacturer and component producer.

Cloud properties retrieved from satellite data are used to diagnose aircraft icing threat in single layer and multilayered ice-over-liquid clouds. The algorithms are being applied in real time to the Geostationary Operational Environmental Satellite (GOES) data over the CONUS with multilayer data available over the eastern CONUS. METEOSAT data are also used to retrieve icing conditions over western Europe. The icing algorithm's methodology and validation are discussed along with future enhancements and plans. The icing risk product is available in image and digital formats on NASA Langley ‘s Cloud and Radiation Products web site, http://www-angler.larc.nasa.gov.

Recent advances in SPE® electrochemical systems have addressed risk areas identified early in the space station program and now support applications that can provide significant potential benefit to ISS in the areas of life support, propulsion and energy storage. Advanced high-pressure systems now under development for navy and aircraft applications can generate oxygen at pressures up to 2000 psi (13.8 MPa) with no moving parts on the oxygen side of the generating system. Zero gravity static phase separators have been developed and are being tested in component and system tests. Cyclic operation of complete systems has been demonstrated.

Off road navigation demands ground robots to traverse complex and often changing terrain. Classification and assessment of terrain can improve path planning strategies by reducing travel time and energy consumption. In this paper we introduce a terrain classification and assessment framework that relies on both exteroceptive and proprioceptive sensor modalities. The robot captures an image of the terrain it is about to traverse and records corresponding vibration data during traversal. These images are manually labelled and used to train a support vector machine (SVM) in an offline training phase. Images have been captured under different lighting conditions and across multiple locations to achieve diversity and robustness to the model. Acceleration data is used to calculate statistical features that capture the roughness of the terrain whereas angular velocities are used to calculate roll and pitch angles experienced by the robot.

This work investigates the effect of engine operating conditions and exhaust sampling conditions (i.e. dilution ratio) on engine-out, nano-scale, particulate matter emissions from a gasoline direct-injection engine during cold-start and warm-up transients. The engine used for this research was an in-line four cylinder, four stroke, wall-guided direct-injection, turbo-charged and inter-cooled 1.6 l gasoline engine. A fast-response particulate spectrometer for exhaust nano-particle measurement up to 1000 nm was utilized, along with a spark-plug mounted pressure transducer for combustion analysis. It was observed that the total particle count decreases during the cold-start transient, and has a distinct relationship with the engine body temperature. Tests have shown that the engine body temperature may be used as a control strategy for engine-out particulate emissions.

This work investigates the effect of a three-way catalytic converter and sampling dilution ratio on nano-scale exhaust particulate matter emissions from a gasoline direct-injection engine during cold-start and warm-up transients. Experimental results are presented from a four cylinder in-line, four stroke, wall-guided direct-injection, turbo-charged and inter-cooled 1.6 litre gasoline engine. A fast-response particulate spectrometer for exhaust nano-particle measurement up to 1000 nm was utilised. It was observed that the three-way catalytic converter had a significant effect on particle number density, reducing the total particle number by up to 65 % over the duration of the cold-start test. The greatest change in particle number density occurred for particles less than 23 nm diameter, with reductions of up to 95 % being observed, whilst the number density for particles above 50 nm diameter exhibited a significant increase.

Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.