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This paper introduces a novel navigation framework for agricultural robots that integrates a global Dubins Traveling Salesman Problem (DTSP) planner with a Nonlinear Model Predictive Control (NMPC) strategy for local path planning and control. The DTSP planner generates a minimum-length, curvature-constrained path through unordered waypoints, while the NMPC controller leverages this path to compute control signals for accurate waypoint tracking. Simulation results on real-world field datasets demonstrate a 16% reduction in path length compared to decoupled methods, highlighting the framework's efficiency in unstructured agricultural environments.
Agricultural robots can now navigate complex fields more efficiently: a new framework slashes path lengths by 16% by tightly integrating global Dubins TSP planning with local NMPC control.
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.
