Many robotics applications require contact with the environment, from traditional pick and place task to legged locomotion. Nevertheless, to increase adaptability to different terrains it is necessary to know its contact properties. These properties can be known beforehand or extracted from contact forces. In this paper a practical evaluation using adaptive filtering techniques to extract the properties from different environmental conditions, with the intention to increase the adaptability of the robot to different environments is presented. We use the well-known linear spring-dashpot model and fit its parameters to four different materials in order to establish which of the reviewed methods (the recursive and windowed least squares algorithms) perform better to describe the material properties. The results show that the recursive least squares provide better tracking performance while the windowed least squares gives a smoother response.

The application of impedance control strategies to modern legged locomotion is analyzed, paying special attention to the concepts behind its implementation which is not straightforward. In order to implement a functional impedance controller for a walking mechanism, the concepts of contact, impact, friction, and impedance have to be merged together. A literature review and a comprehensive analysis are presented compiling all these concepts along with a discussion on position-based versus force-based impedance control approaches, and a theoretical model of a robotic leg in contact with its environment is introduced. A theoretical control scheme for the legs of a general legged robot is also introduced, and some simulations results are presented.

Research on legged locomotion has encountered its limits in human-made actuation technology. Building a structure and an actuation system capable of performing like a biological muscle for the dynamic locomotion of a mid- to large-sized machine is prohibitive. Novel technologies are being explored with the aim of improving actuator performance. This paper describes the development of a robotic leg for agile locomotion. The main aim is to imitate the multidisciplinary performance of the natural muscle by means of hybridizing conventional and new technologies for the development of large power, large force, lightweight, force-controlled, compliant robotic legs. A preliminary leg prototype has been developed. The actuation system is based on the hybrid use of series elasticity and magneto-rheological dampers which provides variable compliance for natural-looking motion and improved interaction with the ground. Experiments with the real leg prototype provide a positive assessment of the target leg features.

Snow-cover parameters are being increasingly used as input to hydrological models. Having an accurate estimation of snow cover characteristics during the snowmelt season is indispensable for an efficient hydrological modeling and snowmelt runoff forecasting. Direct measurements of snow depth at a single station are generally not very useful in making estimates of distribution over large areas, since the measured depth may be highly unrepresentative of the neighboring area because of drifting or blowing. Additionally, the traditional field sampling methods and the ground-based data collection are often very sparse, time consuming, and expensive compared to the coverage provided by remote sensing techniques. At present, most of hydrological models that require snowpack information are using maps obtained by gridding standard point gauge measurements or derived from physically/statistically-based models. In this paper, we used an adaptive neural network system to generate the spatial distribution of snow accumulation from multi-channel SSM/I data in the Northern Midwest of the United States. Five SSM/I channels were used in this experiment (19H, 19V, 22V, 37V, and 85V). Three snow days with high snow accumulation and no precipitation have been selected during the 2001/2002 winter season. Snow depth measurements have been collected from the National Climatic Data Center (NCDC) through the Cooperative Observer Network for the U.S. snow Monitoring. The snow depths have been compiled and gridded into 25 km x 25 km grid to match the final SSM/I resolution.

Snow-cover parameters are being increasingly used as input to hydrological models. An accurate knowledge of the onset of snow melts and snow water equivalent values are important variables in different hydrological applications such as Hooding prediction, reservoir management and agricultural activities. However, the traditional field sampling methods and the ground-based data collection are often very sparse, time consuming, and expensive compared to the coverage provided by remote sensing techniques. Microwave remote sensing techniques have been investigated by numerous researchers using various sensors and have been demonstrated to be effective for monitoring snow pack parameters such as spatial and temporal distribution, snow water equivalent, depth, and snow condition (wet/dry state). Those researches have resulted that the microwave brightness temperature and the microwave backscattering are related to the snow cover structure with different correlation degrees. The primary objective of this research is to produce a spatial estimation of snow water equivalent in a timely fashion with sufficient spatial and temporal resolution using multi-source microwave and optical data. The final product of this project will be an additional tool for flood warning and water resource forecasts, which can be an additional input to the actual hydrological models. The contribution of remote sensing snow related information into the advanced hydrologic prediction system (AHPS) operated by NWS/NOAA (with 4 km grid resolution) will be also evaluated. This paper presents the first step of this project: data collection and evaluation