Probabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains

© 2015, Springer Science+Business Media New York. A probabilistic stable motion planning strategy applicable to reconfigurable robots is presented in this paper. The methodology derives a novel statistical stability criterion from the cumulative distribution of a tip-over metric. The measure is dynamically updated with imprecise terrain information, localization and robot kinematics to plan safety-constrained paths which simultaneously allow the widest possible visibility of the surroundings by simultaneously assuming highest feasible vantage robot configurations. The proposed probabilistic stability metric allows more conservative poses through areas with higher levels of uncertainty, while avoiding unnecessary caution in poses assumed at well-known terrain sections. The implementation with the well known grid based A* algorithm and also a sampling based RRT planner are presented. The validity of the proposed approach is evaluated with a multi-tracked robot fitted with a manipulator arm and a range camera using two challenging elevation terrains data sets: one obtained whilst operating the robot in a mock-up urban search and rescue arena, and the other from a publicly available dataset of a quasi-outdoor rover testing facility

Reliable Industrial IoT-Based Distributed Automation

Reconfigurable manufacturing systems supported by Industrial Internet-of-Things (IIoT) are modular and easily integrable, promoting efficient system/component reconfigurations with minimal downtime. Industrial systems are commonly based on sequential controllers described with Control Interpreted Petri Nets (CIPNs). Existing design methodologies to distribute centralized automation/control tasks focus on maintaining functional properties of the system during the process, while disregarding failures that may occur during execution (e. g., communication packet drops, sensing or actuation failures). Consequently, in this work, we provide a missing link for reliable IIoT-based distributed automation. We introduce a method to transform distributed control models based on CIPNs into Stochastic Reward Nets that enable integration of realistic fault models (e. g., probabilistic link models). We show how to specify desired system properties to enable verification under the adopted communication/fault models, both at design-and run-time; we also show feasibility of runtime verification on the edge, with a continuously updated system model. Our approach is used on real industrial systems, resulting in modifications of local controllers to guarantee reliable system operation in realistic IIoT environments

DEPOSITION CONTROL FOR ELECTROSPUN FIBERS

Electrospinning (ES) is a process for fabricating polymer fibers that have diameters that range from tens of nanons to hundreds of microns, which has been studied for over 100 years. These fibers have been studied in applications such as: the enhancement of mechanical properties including increased sensor sensitivity and increased tensile strength, filtration enhancement, drug delivery systems, and as a lithography masking material. In order to increase the effectiveness of ES, a real time feedback control mechanism to measure fiber diameters is needed. Currently only post-process methods of measuring fiber morphology, such as scanning electron microscopy or transmission electron microscopy, are used to measure ES fibers. Many parameters including: separation distance, applied voltage, polymer viscosity, polymer molecular weight, and flow rates are used to control fiber morphology. Using these parameters in combination with a feedback control mechanism, a multiple-input multiple-output control mechanism could be developed. By using laser extinction tomography, a device was built to measure fiber diameters during deposition. The laser diagnostic device (LaD) has been able to measure the laser extinction while scanning through fiber depositions with limited repeatability