Multi-Waypoint Path Planning and Motion Control for Non-holonomic Mobile Robots in Agricultural Applications

TL;DR

This paper introduces an integrated navigation framework for agricultural robots that combines a global path planner based on the Dubins TSP with a nonlinear model predictive control strategy, improving path efficiency and control accuracy.

Contribution

It presents a novel combined approach using DTSP and NMPC for non-holonomic robots in agriculture, optimizing path length and adherence to constraints.

Findings

Coupled DTSP and NMPC reduce path length by 16%.

System ensures smooth, curvature-constrained paths.

Effective in real-world agricultural scenarios.

Abstract

There is a growing demand for autonomous mobile robots capable of navigating unstructured agricultural environments. Tasks such as weed control in meadows require efficient path planning through an unordered set of coordinates while minimizing travel distance and adhering to curvature constraints to prevent soil damage and protect vegetation. This paper presents an integrated navigation framework combining a global path planner based on the Dubins Traveling Salesman Problem (DTSP) with a Nonlinear Model Predictive Control (NMPC) strategy for local path planning and control. The DTSP generates a minimum-length, curvature-constrained path that efficiently visits all targets, while the NMPC leverages this path to compute control signals to accurately reach each waypoint. The system's performance was validated through comparative simulation analysis on real-world field datasets, demonstrating that the coupled DTSP-based planner produced smoother and shorter paths, with a reduction of about 16% in the provided scenario, compared to decoupled methods. Based thereon, the NMPC controller effectively steered the robot to the desired waypoints, while locally optimizing the trajectory and ensuring adherence to constraints. These findings demonstrate the potential of the proposed framework for efficient autonomous navigation in agricultural environments.