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Dissertation Defense

Energy and Mobility Management of a Ground Robot to Increase Operational Capacity

John Broderick

The operational capacity of an unmanned ground vehicle (UGV) is limited by many different challenges. Limited energy storage and loss of wheel traction can interrupt completion of the robot's mission. To prevent these disruptions to robot operation, three different problems are considered: trajectory planning for an area coverage mission with energy considerations, efficient power management for a hybrid power system, and traction control to prevent wheel slip.
An area coverage mission consists of driving the robot within sensor range of every point in a region. To plan operation, the energy usage characteristics of the robot are required. A method to determine motor efficiency and energy usage is presented. To balance the mission goals and the energy required for operation, a novel cost function, weighting motor torques, area covered and motor efficiency, is used to plan the trajectory. This trajectory is constrained to follow a coverage path planned using existing techniques. We show how the cost function can be used to tradeoff between energy usage and time required to complete the mission.
To increase onboard energy storage, we propose a hybrid power system for a UGV. This combination of power sources requires additional control algorithms to determine which sources should be used throughout the mission. Our control algorithm is based on forming a model of the hybrid power system with power demands from a particular mission. To make the control optimization problem tractable, the model is simplified by using averaged dynamics. Using this model, power management is optimized to limit energy losses. Simulation and Experimental results with a battery/fuel cell power system are presented and show 5% decrease in energy usage compared to a baseline control strategy.
Both lateral and longitudinal wheel slip must be controlled to prevent loss of traction. Using a slipping UGV model, based on automotive friction models, we develop two control algorithms: a novel switching controller and a sliding mode controller. The switching controller considers both lateral and longitudinal wheel slip and complete turning maneuvers. The sliding mode controller, based on automotive techniques, only controls longitudinal wheel slip, possibly losing traction while turning.

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