Review article: locomotion systems for ground mobile robots in unstructured environments

Abstract. The world market of mobile robotics is expected to increase substantially in the next 20 yr, surpassing the market of industrial robotics in terms of units and sales. Important fields of application are homeland security, surveillance, demining, reconnaissance in dangerous situations, and agriculture. The design of the locomotion systems of mobile robots for unstructured environments is generally complex, particularly when they are required to move on uneven or soft terrains, or to climb obstacles. This paper sets out to analyse the state-of-the-art of locomotion mechanisms for ground mobile robots, focussing on solutions for unstructured environments, in order to help designers to select the optimal solution for specific operating requirements. The three main categories of locomotion systems (wheeled - W, tracked - T and legged - L) and the four hybrid categories that can be derived by combining these main locomotion systems are discussed with reference to maximum speed, obstacle-crossing capability, step/stair climbing capability, slope climbing capability, walking capability on soft terrains, walking capability on uneven terrains, energy efficiency, mechanical complexity, control complexity and technology readiness. The current and future trends of mobile robotics are also outlined

Application of the Rotation Matrix Natural Invariants to Impedance Control of Rotational Parallel Robots

Force control of parallel robots with rotational degrees of freedom through impedance algorithms is considerably influenced by the representation method of the end-effector orientation. Using the natural invariants of the rotation matrix and the angular velocity vector in the impedance control law has some theoretical advantages, which derive from the Euclidean-geometric meaning of these entities. These benefits are particularly evident in case of robotic architectures with three rotational degrees of freedom (serial or parallel wrists with spherical motion). The behaviour of a 3-CPU parallel robot controlled by an impedance algorithm based on this concepts is assessed through multibody simulations, and the results confirm the effectiveness of the proposed approach

Transient Stability Analysis of the SeCRETS Experiment in SULTAN

We present here the results of the analysis of the stability experiment SeCRETS, performed on two Nb$_{3}$Sn cable-in-conduit conductors with the same amount of total copper stabilizer, but different degree of segregation. The model used for the analysis, including superconducting strands, conductor jacket and helium, is solved with the code GandalfTM. We obtain a qualitative agreement of simulation results and experimental values. The simulation results confirm that in the operation regime explored in the experiment the segregated copper is not effective for stability. The details of the current sharing and the approximation taken for the transient heat transfer are shown to be critical for the interpretation

Application of the Code THEA to the CONDOPT Experiment in SULTAN

The CONDOPT (CONDuctor OPTimization) experiment has been recently completed in SULTAN. The current sharing behaviour of Nb$_{3}$Sn samples was assessed as a function of the number of cyclic loads experienced during current sweeps in a 10 T background field. We present here results of a computer analysis performed with the code THEATM (for consistent Thermal, Hydraulic and Electric Analysis) in support of the interpretation of the experimental results. We focus in particular on the critical current and current sharing temperature runs, providing details on the features and effects of current distribution among cable sub-stages

Prediction of forest aboveground biomass using multitemporal multispectral remote sensing data

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal informatio

Towards learning free naive bayes nearest neighbor-based domain adaptation

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015