A Sampling-Based Model Predictive Control Approachto Motion Planning Forautonomous Underwater Vehicles

• Other Authors: Caldwell, Charmane Venda, 1976- (authoraut)
• Format: Others
• Language: English; English
• Published: Florida State University
• Subjects: Electrical engineering; Computer engineering
• Online Access: http://purl.flvc.org/fsu/fd/FSU_migr_etd-4518

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. === A Dissertation submitted to the Department of Electrical and Computer Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy. === Summer Semester, 2011. === April 21, 2011. === Autonomous Underwater Vehicle, Model Predictive Control, Sampling-Based Method, Motion Planning, Path Planning === Includes bibliographical references. === Emmanuel G. Collins, Jr., Professor Co-Directing Dissertation; Rodney G. Roberts, Professor Co-Directing Dissertation; David Cartes, University Representative; Linda S. DeBrunner, Committee Member.