Optimization Based Solutions for Control and State Estimation in Non-holonomic Mobile Robots: Stability, Distributed Control, and Relative Localization

TL;DR

This paper develops and analyzes optimization-based control and state estimation methods for non-holonomic mobile robots, emphasizing stability, distributed control, and relative localization to enhance autonomous robot performance.

Contribution

It introduces novel optimization-based solutions tailored for constrained, nonlinear non-holonomic robots, with real-time implementation and stability analysis.

Findings

Effective control and estimation algorithms demonstrated

Real-time implementation achieved on embedded systems

Enhanced stability and localization accuracy

Abstract

Interest in designing, manufacturing, and using autonomous robots has been rapidly growing during the most recent decade. The main motivation for this interest is the wide range of potential applications these autonomous systems can serve in. The applications include, but are not limited to, area coverage, patrolling missions, perimeter surveillance, search and rescue missions, and situational awareness. In this thesis, the area of control and state estimation in non-holonomic mobile robots is tackled. Herein, optimization based solutions for control and state estimation are designed, analyzed, and implemented to such systems. One of the main motivations for considering such solutions is their ability of handling constrained and nonlinear systems such as non-holonomic mobile robots. Moreover, the recent developments in dynamic optimization algorithms as well as in computer processing facilitated the real-time implementation of such optimization based methods in embedded computer systems.