Trajectory Planning Based on Bézier Curve and Q-Network

This work provides a novel use of trajectory planning in agricultural harvesting, with a particular emphasis on harvesting path optimization utilizing Bézier curve navigation and reinforcement learning. The goal of the agent is to learn adaptive trajectories that take into account geographical limits and obstacle-rich situations in the agricultural setting, where efficient harvesting is critical. The suggested method makes use of a Q-network to steer the harvesting machinery along rounded Bézier curves. It then dynamically modifies the pathways to go around obstructions, maximize yield collection, and reduce crop damage. The reward structure's inclusion of collision penalties incentivize the agent to create clever obstacle avoidance techniques. Epsilon-greedy approach with decay mechanism fine-tunes the exploration-exploitation balance. Metrics for evaluation such as exploration, collision rate, and path length. Trajectory visualizations of the agent offer important insights into the flexibility and effectiveness of the suggested method in practical agricultural situations, demonstrating its potential to improve harvesting operations.