基于深度强化学习的茶芽采摘三维路径规划方法

Deep Reinforcement Learning-based Three-dimensional Path Planning Algorithm for Tea Bud Picking

摘要:

路径规划是决定自动化茶芽采摘机器人采摘效率的核心决策环节。针对自动茶芽采摘路径规划中三维空间建模困难、实时响应要求高以及对动态节点规模适应性差等问题,将茶芽采摘任务建模为三维旅行商问题,并提出了一种基于深度强化学习的茶芽采摘三维路径规划方法。本研究采用零填充策略构建了参数化的三维茶芽数据集,设计了动态掩码机制以确保路径的有效性,引入了长短期记忆模块来动态融合历史路径信息,开发了约束多头自注意力机制以提高决策准确性,并基于演员-评论家(Actor-Critic)框架稳定模型训练。对比试验表明,所提出的算法与精确的Concorde算法之间的相对最优性差距控制在2%以内,而在处理80个茶叶芽的情况下计算时间仅为Concorde算法的1.7%。该算法优于经典的启发式算法和其他基于深度强化学习的算法,在实际茶园场景中表现稳定,为采茶机器人路径规划提供了高效方案,可迁移至其他精准采摘任务,也为三维路径规划提供了理论基础。

Abstract:

Picking path planning is the core decision-making link that determines the harvesting efficiency of automated tea bud picking robots. Aiming at the problems of difficult three-dimensional spatial modeling, high real-time response requirements and poor adaptability to dynamic node scales in automatic tea bud picking path planning, the tea bud picking task was modelled as a three-dimensional traveling salesman problem (3D-TSP), and a deep reinforcement learning-based 3D path planning algorithm was proposed for tea bud picking. A parametric 3D tea bud dataset with a zero-padding strategy was constructed, a dynamic masking mechanism was designed to ensure path validity, a long short-term memory (LSTM) module was introduced to dynamically fuse historical path information, a constrained multi-head self-attention mechanism was developed to improve decision-making accuracy, and the stability model training was done based on the Actor-Critic framework. Comparative experiments showed that the relative optimality gap between the proposed algorithm and the exact Concorde algorithm was controlled within 2% , and its computation time in the scenario with 80 tea buds was only 1.7% of that of Concorde. The algorithm outperformed classical heuristic algorithms and other deep reinforcement learning-based algorithms, maintained stable performance in real tea garden scenarios, and provided an efficient solution for tea picking robot path planning.