Research on the Agricultural Machinery Path Tracking Method Based on Deep Reinforcement Learning

Abstract

With the rapid development of information technology, industry and service industries have achieved rapid development in recent years. Then, looking at the development of agriculture, the popularity of informatization lags far behind industry and service industries, directly hindering the digital development of agriculture. Starting from the current agricultural machinery driving operation scene, this paper carried out a simplified research on the traditional agricultural machinery driving operation method through the agricultural machinery kinematics model, and based on the related theory of deep reinforcement learning to study the agricultural machinery path tracking in the agricultural operation scene, it carried out the controller design, built the agricultural machinery autonomous path tracking framework operating mechanism under deep reinforcement learning, and further researched through experimental design and found that the agricultural machinery autonomous path tracking control can achieve better automatic control after empirical learning. I-DQN algorithm enables agricultural robots to adapt to the environment faster when performing path tracking, which improves the performance of path tracking. It has important guiding significance for further promoting the automatic navigation and control of agricultural machinery to realize the efficient operation of agricultural mechanization.

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

“…. χ = a χ + bu (10) According to Equations ( 8) and (10), path tracking can be accomplished by utilizing MPC to obtain the appropriate amount of control at the lowest possible cost, hence lowering the tracking error.…”

“…From Figure 6 (set Np = 10 and Nc = 3), Npre should be increased appropriately but not excessively as the curvature grows. The tracking effect in Figure 6 is ranked as Npre(0) < Npre(1) < Npre(2) < Npre(4) > Npre(5) > Npre (10), with Npre = 4 having the best effect.…”

“…Path tracking control is at the heart of autonomous driving technology, which is widely used in farming, planting, management, harvesting, transportation, and other aspects of agricultural production to improve work quality and efficiency and to reduce the waste of agricultural production materials [2]. Typical path tracking control methods include geometric model-based PP [3] and Stanley [4], etc., kinematic and dynamic model-based PID [5], LQR [6], SMC [7], and MPC [8], etc., as well as model-free based fuzzy control [9], reinforcement learning [10], and neural networks [11], etc.…”

Research on path planning and obstacle avoidance for unmanned platforms based on reinforcement learning

“…The pure pursuit algorithm is a classical path tracking algorithm that selects a target point on a reference path and computes controller outputs to smoothly move towards the target point along the reference path [7].…”

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

“…Liu et al adopt the fuzzy clustering method to realize the obstacle avoidance of the robot, and fuse multiple sensors to collect information to assist obstacle avoidance, which performs well in physical experiments [39]. Li et al apply a DQN algorithm to complete the intelligent path tracking task based on the agricultural machinery driving scenario, and maintain a fast convergence speed and good stability in the dynamic environment [40].…”