Integrated Opposite-Phase Kinematics, Hybrid-A* Planning, and MPPI Control for Autonomous Navigation in Agricultural Environment
This paper presents the integration and evaluation of an autonomous navigation system for a four-wheel steering (4WS) agricultural robot operating in opposite-phase steering mode within simulated blueberry plantation environments. The system combines opposite-phase steering kinematic modeling with wheel-based odometry fused via an Extended Kalman Filter, graph-based SLAM for environment mapping, Adaptive Monte Carlo Localization for pose estimation, Hybrid-A* path planning for kinematically-constrained trajectory generation, and Model Predictive Path Integral control for local motion execution, all orchestrated through a behavior tree framework within the ROS 2 Navigation Stack (Nav2). Three navigation tasks: perimeter traversal, inter-row serpentine coverage, and an extended multi-region circuit were conducted in a Gazebo-based simulation replicating blueberry row-crop geometry. The system completed all navigation goals without triggering recovery behaviors, validating the feasibility of the Nav2 framework for structured agricultural navigation.