A Step-by-step Guide on Nonlinear Model Predictive Control for Safe Mobile Robot Navigation
Dennis Benders, Laura Ferranti, Johannes K\"ohler

TL;DR
This paper provides a practical, step-by-step guide for implementing nonlinear model predictive control (NMPC) to ensure safe navigation of mobile robots in obstacle-rich environments, emphasizing safety and real-world applicability.
Contribution
It offers a detailed methodology for applying NMPC in mobile robotics, bridging theoretical concepts with practical implementation for safety guarantees.
Findings
Demonstrates how NMPC can ensure collision avoidance
Provides mathematical proofs for safety and stability
Guides from theory to real-world robotic control
Abstract
Designing a model predictive control (MPC) scheme that enables a mobile robot to safely navigate through an obstacle-filled environment is a complicated yet essential task in robotics. In this technical report, safety refers to ensuring that the robot respects state and input constraints while avoiding collisions with obstacles despite the presence of disturbances and measurement noise. This report offers a step-by-step approach to implementing nonlinear model predictive control (NMPC) schemes addressing these safety requirements. Numerous books and survey papers provide comprehensive overviews of linear MPC (LMPC), NMPC, and their applications in various domains, including robotics. This report does not aim to replicate those exhaustive reviews. Instead, it focuses specifically on NMPC as a foundation for safe mobile robot navigation. The goal is to provide a practical and accessible…
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Taxonomy
TopicsAdvanced Control Systems Optimization · Robotic Path Planning Algorithms · Adaptive Control of Nonlinear Systems
