Abstract
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 vehicle can feasibly traverse. As a result, Sampling-Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling-based motion planning with model predictive control (MPC) while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization (e.g., A*) in place of standard numerical optimization. This formulation of MPC readily applies to nonlinear systems and avoids the local minima which can cause a vehicle to become immobilized behind obstacles. The SBMPC algorithm is applied to an AUV in a 2D cluttered environment and an AUV in a common local minima problem. The algorithm is then used on a full kinematic model to demonstrate the benefits.
Highlights
There have been non-mine related crises in the past that have caused setbacks at U.S ports: the Exxon Valdez spill of 1989, which cost more than $2.5 billion to clean up, and the dock workers strike of 2002, which resulted in a loss of $1.9 billion dollars a day [2]
The United States has over 360 ports that comprise more than 90% of the U.S export and import industry [1]
This paper presents results for motion planning with a kinematic model
Summary
The United States has over 360 ports that comprise more than 90% of the U.S export and import industry [1]. SBMPC allows a model to be considered online while simultaneously determining the optimal control input and a kinematically or dynamically feasible trajectory of the AUV. MPC generally works by solving an optimization problem at every time step k to determine control inputs for the N steps, known as the prediction horizon This optimal control sequence is determined by using the system model to predict the potential system response, which is evaluated by the cost function J. Goal-directed optimization methods implicitlyconsider the goal through the use of a function that computes a rigorous lower bound of the cost from a particular state to G This function, often referred to as an “optimistic heuristic” in the robotics literature, is eventually replaced by actual cost values based on the predictions and does not appear in the final cost function. The Open List is implemented as a heap so that the lowest cost node that has not been expanded is on top
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