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

2025-07-31

AI Summary

Path planning and motion control for nonholonomic mobile robots in unstructured farmland pose significant challenges due to geometric constraints, dynamic terrain conditions, and stringent agricultural requirements (e.g., soil protection and precise waypoint tracking). Method: This paper proposes a navigation framework integrating global path optimization with local trajectory tracking. It synergistically combines the Dubins Traveling Salesman Problem (DTSP) and nonlinear model predictive control (NMPC): DTSP computes curvature-constrained globally optimal paths, while NMPC enables real-time trajectory optimization and high-accuracy waypoint following under dynamic environmental uncertainties. Contribution/Results: The key innovation lies in a global–local joint optimization mechanism that jointly satisfies geometric feasibility, soil-compaction avoidance, and control precision. Simulation results demonstrate that the proposed method reduces path length by 16% compared to conventional decoupled approaches, ensures continuous and smooth curvature profiles, and achieves waypoint arrival errors below 0.05 m—substantially enhancing navigation efficiency and operational safety for agricultural tasks such as weed control.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Path planning for non-holonomic robots in agriculture
• Minimizing travel distance with curvature constraints
• Integrated navigation for efficient multi-waypoint control

Innovation

• DTSP for global path planning
• NMPC for local control
• Integrated navigation framework