Dynamics simulation and autonomous driving algorithm integration of unmanned harvester based on TruckSim/Simulink

Area of Science:

• Agricultural Engineering
• Robotics
• Control Systems

Background:

• Small unmanned harvesters require enhanced path tracking and dynamic adaptability for complex farmland operations.
• Existing simulation and validation methods often face limitations in accuracy and efficiency.

Purpose of the Study:

• To develop a simulation and autonomous driving algorithm framework for small unmanned harvesters.
• To improve path tracking accuracy and dynamic adaptability in complex agricultural environments.
• To create an efficient platform for testing and validating autonomous driving algorithms, reducing development cycles and costs.

Main Methods:

• Integration of TruckSim for high-precision dynamic simulation and Simulink for algorithm development.
• Implementation of a hybrid A* algorithm with dual heuristic search for path planning.
• Design of a Proportional-Integral-Derivative (PID) controller for path tracking and speed control.
• Development of an Extended Kalman Filter (EKF) for dynamic road adhesion coefficient identification.
• Incorporation of a PID-based lane-keeping algorithm with steering-speed coordination.

Main Results:

• A comprehensive simulation platform accurately modeling harvester behavior in agricultural settings.
• Optimized path planning and control strategies for enhanced operational performance.
• Dynamic estimation of road conditions and adaptive control adjustments.
• Significantly improved operational stability and robustness in various farmland environments.

Conclusions:

• The proposed framework provides an innovative simulation tool and an effective algorithm validation platform.
• Advancements in simulation and control algorithms contribute to the development of intelligent agricultural equipment.
• The research successfully enhances the capabilities of small unmanned harvesters for complex agricultural tasks.