Autonomous unmanned ground vehicles (UGVs) are beginning to play a more critical role in military operations. As the size of the fighting forces continues to draw down, the U.S. and coalition partner Armed Forces will become increasingly reliant on UGVs to perform mission-critical roles. These roles range from squad-level manned-unmanned teaming to large-scale autonomous convoy operations. However, as more UGVs with increasing levels of autonomy are entering the field, tools for accurately predicting these UGVs performance and capabilities are lacking. In particular, the mobility of autonomous UGVs is a largely unsolved problem. While legacy tools for predicting ground vehicle mobility are available for both assessing performance and planning operations, in particular the NATO Reference Mobility Model, no such toolset exists for autonomous UGVs. Once autonomy comes into play, ground vehicle mechanical-mobility is no longer enough to characterize vehicle mobility performance. Not only will vehicle-terrain interactions and driver concerns impact mobility, but sensor-environment interactions will also affect mobility. UGV mobility will depend in a large part on the sensor data available to drive the UGVs autonomy algorithms. A limited amount of research has been focused on the concept of perception-based mobility to date. To that end, the presented work will provide a review of the tools and methods developed thus far for modeling, simulating, and assessing autonomous mobility for UGVs. This review will highlight both the modifications being made to current mobility modeling tools and new tools in development specifically for autonomous mobility modeling. In light of this review, areas of current need will also be highlighted, and recommended steps forward will be proposed.
Energy Harvesting is a powerful process that deals with exploring different possible ways of converting energy dispersed in the environment into more useful form of energy, essentially electrical energy. Piezoelectric materials are known for their ability of transferring mechanical energy into electrical energy or vice versa. Our work takes advantage of piezoelectric material’s properties to covert thermal energy into electrical energy in an oscillating heat pipe. Specific interest in an oscillating heat pipe has relevance to energy harvesting for low power generation suitable for remote electronics operation as well as low-power heat reclamation for electronic packaging. The aim of this paper is develop a 2D multi-physics design analysis model that aids in predicting electrical power generation inherent to an oscillating heat pipe. The experimental design shows a piezoelectric patch with fixed configuration, attached inside an oscillating heat pipe and its behavior when subjected to the oscillating fluid pressure was observed. Numerical analysis of the model depicting the similar behavior was done using a multiphysics FEA software. The numerical model consists of a threeway physics interaction that takes into account fluid flow, solid mechanics, and electrical response of the harvester circuit.
A series of experiments were conducted to investigate and characterize the concept of ferrofluidic induction - a process for generating electrical power via cyclic oscillation of ferrofluid (iron-based nanofluid) through a solenoid. Experimental parameters include: number of bias magnets, magnet spacing, solenoid core, fluid pulse frequency and ferrofluid-particle diameter. A peristaltic pump was used to cyclically drive two aqueous ferrofluids, consisting of 7-10 nm iron-oxide particles and commercially-available hydroxyl-coated magnetic beads (~800 nm), respectively. The solutions were pulsated at 3, 6, and 10 Hz through 3.2 mm internal diameter Tygon tubing. A 1000 turn copper-wire solenoid was placed around the tube 45 cm away from the pump. The experimental results indicate that the ferrofluid is capable of inducing a maximum electric potential of approximately +/- 20 μV across the solenoid during its cyclic passage. As the frequency of the pulsating flow increased, the ferro-nanoparticle diameter increased, or the bias magnet separation decreased, the induced voltage increased. The type of solenoid core material (copper or plastic) did not have a discernible effect on induction. These results demonstrate the feasibility of ferrofluidic induction and provide insight into its dependence on fluid/flow parameters. Such fluidic/magneto-coupling can be exploited for energy harvesting and/or conversion system design for a variety of applications.