A Framework for Optimal Path Planning and Nonlinear Guidance for Autonomous Mobile Robots

Abstract

The purpose of this research is to investigate methods and technology for enhancing autonomous capabilities for mobile robots. The measures of autonomy which are specifically covered in this dissertation pertain to a mobile robot’s ability to make decisions and act, in other words guidance and control. This dissertation puts forth a framework using optimal path planning and nonlinear guidance techniques to address these matters. The path plans are synthesized using a numerical navigation function algorithm that will form its potential contour levels based on the “minimum control effort” of the system. Additionally, extensions of the path planning algorithm in the presence of uncertainty using modified versions of the RRT* and D* algorithms are studied. Then, an improved nonlinear model predictive control (NMPC) approach is employed to generate high-level guidance commands for the mobile robot to track a trajectory fitted along the path plan leading to the goal. A backstepping-like nonlinear guidance law is also implemented for comparison with the NMPC formulation. Furthermore, a cooperative control policy, making use of a combination of artificial potential functions and the numerical navigation function, is devised to guide multiple mobile robots in cooperative aggregation and social foraging tasks. The results of this research are verified in simulation and validated experimentally using the mobile robot testing platforms in the Aerospace Systems Laboratory at The University of Texas at Arlington.