Modeling of Wheel-Soil Interaction for Small Ground Vehicles Operating on Granular Soil.

Unmanned ground vehicles continue to increase in importance for many industries, from planetary exploration to military defense. These vehicles require significantly fewer resources compared to manned vehicles while reducing risks to human life. Terramechanics can aid in the design and operation of small vehicles to help ensure they do not become immobilized due to limited traction or energy depletion. In this dissertation methods to improve terramechanics modeling for vehicle design and control of small unmanned ground vehicles (SUGVs) on granular soil are studied. Various techniques are developed to improve the computational speed and modeling capability for two terramechanics methods. In addition, a new terramechanics method is developed that incorporates both computational efficiency and modeling capability. First, two techniques for improving the computation performance of the semi-empirical Bekker terramechanics method are developed. The first technique stores Bekker calculations offline in lookup tables. The second technique approximates the stress distributions along the wheel-soil interface. These techniques drastically improve computation speed but do not address its empirical nature or assumption of steady-state operation. Next, the discrete element method (DEM) is modified and tuned to match soil test data, evaluated against the Bekker method, and used to determine the influence of rough terrain on SUGV performance. A velocity-dependent rolling resistance term is developed that reduced DEM simulation error for soil tests. DEM simulation shows that surface roughness can potentially have a significant impact on SUGV performance. DEM has many advantages compared to the Bekker method, including better locomotion prediction, however large computation costs limit its applicability for design and control. Finally, a surrogate DEM model (S-DEM) is developed to maintain the simulation accuracy and capabilities of DEM with reduced computation costs. This marks one of the first surrogate models developed for DEM, and the first known model developed for terramechanics. S-DEM stores wheel-soil interaction forces and soil velocities extracted from DEM simulations. S-DEM reproduces drawbar pull and driving torque for wheel locomotion on flat and rough terrain, though wheel sinkage error can be significant. Computational costs are reduced by three orders of magnitude, bringing the benefits of DEM modeling to vehicle design and control.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108811/1/wsmithw_1.pd

Acceptance Testing and Energy-based Mission Reliability in Unmanned Ground Vehicles.

The objective of this research is to explore and develop new methodologies and techniques to improve UGV mission reliability. This dissertation focuses on two research issues that are critical in the following UGV deployment phases: (1) prior to field deployment to remove design deficiencies; and (2) during field usage to prevent mission failures. Four specific research topics are accomplished. The first topic focuses on simulation-based acceptance testing. A general framework is proposed to integrate dynamic and static simulations. Statistical hypothesis testing is used to compare static and dynamic simulations to determine when a simple static simulation can be used to replace the complex dynamic simulation. Results show that the static simulation can be used when a failure mechanism is not significantly affected by the dynamic characteristics of the vehicle. The remaining research topics aim at prevention of operational failures due to unexpected energy depletion. A model-based Bayesian prediction framework integrated with a dynamic vehicle model is proposed in the second research topic, which improves traditional approaches for estimation and prediction. The Bayesian framework combines mission prior knowledge with real-time measurements for adaptive prediction of end-of-mission energy requirement. Experimental studies were conducted, which validated and demonstrated the advantages of the framework on roads with different surface types and grades. The third research topic, entitled real-time energy reliable path planning, builds upon the above mentioned prediction framework to identify the most energy reliable path in a stochastic network with unknown and correlated arc lengths. Since traditional sequential optimization techniques cannot be directly applied to this problem, a heuristic approach based on two stage exploration/exploitation is proposed to identify the most reliable path. The framework, which minimizes the cost of exploration, outperforms traditional path planning approaches. In the final research topic, the impact of operator driving style on mission energy requirements is investigated using statistical response surface. While the previous topics help with overall mission planning regardless of the operator’s driving style, here, improving the driving style to increase energy availability is studied. The optimal drive cycle that minimizes energy consumption and procedures for reduction of energy consumption are proposed.PhDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107075/1/sadrpour_1.pd