针对名优茶采摘作业效率低、现有研究在双臂任务分配与路径规划方面尚显不足等问题,以共轨龙门式双臂采摘平台为试验载体,提出一种面向茶芽采摘的双臂协同任务规划与调度方法。针对多目标茶芽采摘任务,提出基于GA-2Opt的混合路径规划算法,并结合耗时引导下的区域划分策略,实现双臂负载的自适应均衡;同时,引入基于时间序列的主动防碰撞调度机制,在规划层缓解双臂系统的干涉冲突。结果表明,该系统在实地作业实验验证中的采摘成功率约为79.7%,平均双臂并行作业率达到93.3%,相比于传统划分策略,显著降低了总作业时间,单个茶芽采摘平均作业周期为1.39 s,验证了协同策略在非结构化环境下的鲁棒性,为名优茶自动化采摘提供了高效解决方案。
Abstract
In light of the low efficiency of premium tea harvesting and the insufficient research on dual-arm task allocation and path planning in existing studies, this study used a common-rail gantry dual-arm harvesting platform as the experimental carrier and proposed a dual-arm collaborative task planning and scheduling method for tea bud harvesting. For multi-target tea bud harvesting tasks, a hybrid path planning algorithm based on GA-2Opt was proposed and combined with a region partitioning strategy guided by time costs to achieve adaptive load balancing between the two arms. Meanwhile, an active collision avoidance scheduling mechanism based on temporal sequences was introduced to mitigate interference conflicts in the dual-arm system at the planning layer. Field experiments demonstrate a picking success rate of approximately 79.7% and an average dual-arm parallelism rate of 93.3%. Compared with traditional partitioning strategies, the system significantly reduced total operating time, with an average picking cycle time of 1.39 s per tea bud. These findings validate the robustness of the collaborative strategy in unstructured environments, and provide a high-efficiency solution for automated premium tea harvesting.
关键词
双臂协同 /
茶芽采摘 /
路径规划 /
任务分配
Key words
dual-arm collaboration /
tea bud picking /
path planning /
task allocation
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基金
山东省重点研发计划（2023CXPT085）; 国家自然科学基金（52275347）
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