Abstract
<h2>Abstract</h2> Sampling-Based Model Predictive Control (SBMPO) is a novel nonlinear MPC (NMPC) approach that enables motion planning with dynamic models. This tool is also well suited to solve traditional MPC problems and has been tested in various situations ranging from robotics, task scheduling, resource management, combustion processes, and general optimization.
Highlights
Sampling-Based Model Predictive Optimization (SBMPO) generates a cost-optimal trajectory of feasible control inputs, which, if executed, enables mobile robots, manipulators, aerial platforms, or general dynamical systems to operate intelligently in complex environments for challenging tasks
The cost functions employed can involve physically motivated quantities such as distance, time, or energy. This algorithm has successfully demonstrated planning capabilities on a variety of mobile robots such as autonomous ground vehicles (AGVs), autonomous air vehicles (AAVs), autonomous underwater vehicles (AUVs), and legged robotic platforms operating under complex environmental constraints [1,2,3,4,5,6,7]
Sampling-Based Model Predictive Control (SBMPO) is comprised of model specific components, and the optimization engine
Summary
Sampling-Based Model Predictive Optimization (SBMPO) generates a cost-optimal trajectory of feasible control inputs, which, if executed, enables mobile robots, manipulators, aerial platforms, or general dynamical systems to operate intelligently in complex environments for challenging tasks. This trajectory directly accounts for situation-specific input and output constraints, system dynamics, and efficient sampling for effective scaling in high dimensions. Sampling based approaches are often ‘‘anytime’’ in nature, meaning the algorithm can be terminated at any time and still return a feasible, if not optimal, solution These algorithms are more capable of accounting for complexities due to reduced memory requirement. While complexities differ based on application, several typical situational constraints are included in our software and documentation on adding new modules is found in the README file at: https://github.com/DIRECTLab/SBMPO/
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