The emergence of harvesting robotics offers a promising solution to the issue of limited agricultural labor resources and the increasing demand for fruits. Despite notable advancements in the field of harvesting robotics, the utilization of such technology in orchards is still limited. The key challenge is to improve operational efficiency. Taking into account inner-arm conflicts, couplings of DoFs, and dynamic tasks, we propose a task planning strategy for a harvesting robot with four arms in this paper. The proposed method employs a Markov game framework to formulate the four-arm robotic harvesting task, which avoids the computational complexity of solving an NP-hard scheduling problem. Furthermore, a multi-agent reinforcement learning (MARL) structure with a fully centralized collaboration protocol is used to train a MARL-based task planning network. Several simulations and orchard experiments are conducted to validate the effectiveness of the proposed method for a multi-arm harvesting robot in comparison with the existing method.

翻译：采摘机器人的出现为解决农业劳动力资源有限和水果需求增长的问题提供了有前途的解决方案。尽管采摘机器人领域取得了显著进展，但此类技术在果园中的应用仍然有限。关键挑战在于提升作业效率。本文考虑臂间冲突、自由度耦合及动态任务因素，提出了一种四臂采摘机器人的任务规划策略。该方法采用马尔可夫博弈框架对四臂机器人采摘任务进行建模，避免了求解NP难调度问题的计算复杂性。进一步，采用具有全集中协作协议的多智能体强化学习（MARL）结构，训练基于MARL的任务规划网络。通过多组仿真与果园实验，验证了所提方法相较现有方法在多臂采摘机器人任务中的有效性。