To aid in real-world situations, soft robots need to be able to estimate their state and external interactions based on proprioceptive sensors. Estimating disturbances allows a soft robot to perform desirable force control. However, even in the case of rigid manipulators, force estimation at the end-effector is seen as a non-trivial problem. And indeed, current approaches to address this challenge have shortcomings that prevent their general application. They are often based on simplified soft dynamic models, such as the ones relying on a piece-wise constant curvature approximation or matched rigid-body models that do not represent enough details of the problem. This severely limits applications in complex human-robot interaction. Finite element method (FEM) based modeling allows for predictions of soft robot dynamics in a more generic fashion. Here, using the soft robot modeling capabilities of the frame-work SOFA, we built a detailed FEM model of a multi-segment soft continuum robotic arm composed of compliant deformable materials and fiber-reinforced pressurized actuation chambers. In addition, a model for sensors that provide orientation output is presented. This model is used to establish a state observer for the manipulator. The sensor model is adequate for representing the output of flexible bend sensors as well as orientations provided by IMUs or coming from tracking systems, all of which are popular choices in soft robotics. Model parameters were calibrated to match imperfections of the manual fabrication process using physical experiments. We then solve a quadratic programming inverse statics problem to compute the components of external force that explain the pose mismatch. Our experiments show an average force estimation error of around 1.2%. As the methods proposed are generic, these results are encouraging for the task of building soft robots exhibiting complex, reactive, sensor-based behavior that can be deployed in human-centered environments.

Identifying risk components is crucial to improve product quality. Failure mode and effects analysis as a useful risk assessment method has become a prevalent application in product design. However, the critical data, which contain failure causality relationships (FCRs) between failure modes, importance correlations among risk factors, and customer requirements of the product component, are not considered. This study develops an integrated approach for identifying risky components considering customer requirements and FCRs. First, a quality function deployment is established to characterize the customer requirements under fuzzy assessment semantics. Second, the FCRs between and within the product components are characterized by a directed network model. In this network, the failure modes are modelled as vertices, and the causality relationships between the failure modes are modelled as directed edges. The values of the directed edges are characterized by weighted risk priority numbers, and the weight of risk factors is optimized by a nonlinear programming model. Then, the interactive relationships among failure modes between and within product components are characterized by the internal failure effect and external failure effect. Finally, a real-world case application of wheel loader is conducted to demonstrate the validity and feasibility of the proposed approach. The results have shown that the proposed method is more effective in identifying risk components.

With the rapid industrialization and urbanization in China, the great increases in anthropogenic emissions during the last decades have caused serious air pollution problems greatly influencing public health [1-2]. The Beijing-Tianjin-Hebei (BTH) region, located in central-eastern China, is not only one of China's most economically developed and industrialized regions, but is the area that most frequently experiences haze episodes. In this study, the characteristics of spatial and temporal distribution and variation trends of aerosol optical depth (AOD) on 550 nm, SO2 and NO2 column density during 2002-2013 over BTH region were analyzed by using MODIS, OMI, GOME-2 and SCIAMACHY satellite data [3-5]. With the Geographically Weighted Regression (GWR) model, the driving factors including motor vehicle ownership, electricity output, population and the construction area from the city statistical yearbooks are preliminary analyzed.