Toward Optimizing Path Tracking of Agricultural Mobile Robots with Different Steering Mechanisms

Abstract

This chapter presents preliminary results of a study on the development of a simulation framework designed to investigate and evaluate various path tracking control strategies for agricultural mobile robots. The study focuses on the effect of different steering mechanisms on path tracking performance and explores methods for finding the shortest path covering multiple randomly assigned waypoints. Differential drive, Ackermann steering, and articulated steering mechanisms are examined, highlighting their advantages and limitations in various agricultural field conditions. As a case study, we demonstrate the design and implementation of a PID controller for regulating the steering angle and speed of a differential drive robot to track a reasonable shortest path that connects several random waypoints. Future work on applying model predictive control (MPC) for path tracking tasks is also outlined. Through simulations conducted in MATLAB and CoppeliaSim, insights are provided into the effectiveness of various path tracking control strategies, aiming to enhance the navigation capabilities of agricultural mobile robots in dynamic and unstructured environments. Experimental results and performance evaluations demonstrate the robustness and adaptability of the proposed control strategies, offering valuable insights for the development of autonomous agricultural robotics systems.