Autonomous Weeding Robots: Coordinating Locomotion and Manipulation via Long Short-Term Memory-Proximal Policy Optimization Reinforcement Learning

Autonomous weeding robots offer significant potential for reducing labor and environmental costs in precision agriculture. However, most systems decouple chassis locomotion from manipulator control, limiting efficiency in unstructured environments. We present LSTM-PPO-Weeding, an end-to-end framework unifying mobility and manipulation via memory-augmented reinforcement learning (RL). A high-speed region of interest (ROI) filter abstracts camera input into three-dimensional weed/crop poses, enabling the system to focus solely on task-relevant features. To mitigate challenges arising from occlusion and partial observability, we integrate long short-term memory (LSTM) into the proximal policy optimization (PPO) algorithm, establishing the proposed LSTM-PPO framework. The agent learns coordinated actions through a custom reward function balancing speed and precision. Experiments in simulation and real-world greenhouse settings show that LSTM-PPO-Weeding improves throughput while maintaining high accuracy. Compared to baseline methods, our approach reduces average weeding time by up to 74% with minimal loss in success rate, demonstrating its robustness for agricultural mobile manipulation.