Charmane V. Caldwell

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In this paper a novel method called Sampling-Based Model Predictive Control (SBMPC) is proposed as an efficient MPC algorithm to generate control inputs and system trajectories. The algorithm combines the benefits of sampling-based motion planning with MPC while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms(More)
Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can be difficult when completing complex tasks. Introducing models into the motion planning process can produce paths the(More)
Model Predictive Control (MPC) was originally developed for relatively slow processes in the petroleum and chemical industries and is well known to have difficulties in computing control inputs in real time for processes with fast dynamics. In this paper a novel method called Sampling Based Model Predictive Control (SBMPC) is proposed as a resolution(More)
Path planning is a method that determines a path, consecutive states, between a start state and goal state, LaValle (2006). However, in motion planning that path must be parameterized by time to create a trajectory. Consequently, not only is the path determined, but the time the vehicle moves along the path. To be successful at motion planning, a vehicle(More)
The motors or engines of an autonomous ground vehicles (AGV) have torque and power limitations, which limit their abilities to climb steep hills, which are defined to be hills that have high grade sections in which the vehicle is forced to decelerate. Traversal of a steep hill requires the vehicle to have sufficient momentum before entering the hill. This(More)
Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can be difficult when completing complex tasks. Introducing models into the motion planning process can produce paths the(More)
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