Type of Document	Dissertation
Author	Caldwell, Charmane Venda
Author's Email Address	cvcaldwe@eng.fsu.edu
URN	etd-06172011-144252
Title	A Sampling-Based Model Predictive Control Approach to Motion Planning for Autonomous Underwater Vehicles
Degree	Doctor of Philosophy
Department	Electrical and Computer Engineering, Department of
Advisory Committee	Advisor Name Title Emmanuel Collins Committee Co-Chair Rodney Roberts Committee Co-Chair Linda DeBrunner Committee Member Dave Cartes University Representative
Keywords	Autonomous Underwater Vehicle Model Predictive Control Sampling-Based Method Motion Planning Path Planning
Date of Defense	2011-04-21
Availability	unrestricted
Abstract In recent years there has been a demand from the commercial, research and military industries to complete tedious and hazardous underwater tasks. This has lead to the use of unmanned vehicles, in particular autonomous underwater vehicles (AUVs). To operate in this environment the vehicle must display kinematically and dynamically feasible trajectories. Kinematic feasibility is important to allow for the limited turn radius of an AUV, while dynamic feasibility can take into consideration limited acceleration and braking capabilities due to actuator limitations and vehicle inertia. Model Predictive Control (MPC) is a method that has the ability to systematically handle multi-input multi-output (MIMO) control problems subject to constraints. It finds the control input by optimizing a cost function that incorporates a model of the system to predict future outputs subject to the constraints. This makes MPC a candidate method for AUV trajectory generation. However, traditional MPC has difficulties in computing control inputs in real time for processes with fast dynamics. This research applies a novel MPC approach, called Sampling-Based Model Predictive Control (SBMPC), to generate kinematically or dynamically feasible system trajectories for AUVs. 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 and traditional MPC, namely large computation times and local minimum problems. SBMPC is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization method (e.g., A?) in place of standard nonlinear programming. SBMPC can avoid local minimum, has only two parameters to tune, and has small computational times that allows it to be used online fast systems. A kinematic model, decoupled dynamic model and full dynamic model are incorporated in SBMPC to generate a kinematic and dynamic feasible 3D path. Simulation results demonstrate the efficacy of SBMPC in guiding an autonomous underwater vehicle from a start position to a goal position in regions populated with various types of obstacles.
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