Abstract
This article puts forth a framework using model-based techniques for path planning and guidance for an autonomous mobile robot in a constrained environment. The path plan is synthesized using a numerical navigation function algorithm that will form its potential contour levels based on the “minimum control effort.” Then, an improved nonlinear model predictive control approach is employed to generate high-level guidance commands for the mobile robot to track a trajectory fitted along the planned path leading to the goal. A backstepping-like nonlinear guidance law is also implemented for comparison with the NMPC formulation. Finally, the performance of the resulting framework using both nonlinear guidance techniques is verified in simulation where the environment is constrained by the presence of static obstacles.
Highlights
The focus of this article is to present a framework for path planning and guidance for autonomous mobile robots
navigation function (NF) planners are designed as a special class of artificial potential functions (APFs), wherein the local minimum problem is eliminated
The article introduced a new modelbased path planning algorithm in addition to a nonlinear model predictive control (NMPC) guidance design to form a reliable path planning and guidance framework that was verified in simulation
Summary
The focus of this article is to present a framework for path planning and guidance for autonomous mobile robots. The result, is a path plan that will guide the robot safely to its objective, while considering the control effort to get there as well as how to form its approach to a goal state. This approach will influence the shaping of the NF’s contours based on the final desired reachable state that considers the linear kinematic model of a mobile robot with the minimum energy controller solution.
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