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

Acceptance Testing;Energy-based Reliability;Unmanned Ground Vehicles;Bayesian Prediction;Industrial and Operations Engineering;Mechanical Engineering;Engineering;Industrial & Operations Engineering

Sadrpour, Amir A.Lavieri-Rodriguez, Mariel ;

University of Michigan

关键词: Acceptance Testing;    Energy-based Reliability;    Unmanned Ground Vehicles;    Bayesian Prediction;    Industrial and Operations Engineering;    Mechanical Engineering;    Engineering;    Industrial & Operations Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/107075/sadrpour_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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.