通过学习人机协同效应，优化制造中任务分配与调度。
Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts
- 基于贝叶斯方法学习任务并行时的人机耦合效应
- 实测可减少18%流程执行时间，提升人机安全距离
- 适合需要高效人机协作的智能制造场景
协作机器人系统利用异构智能体实现敏捷生产。有效协调对避免效率损失和操作员风险至关重要。本文提出一种基于混合整数非线性规划的人机感知任务分配与调度模型，从任务规划阶段优化效率与安全。该方法通过贝叶斯估计从历史执行数据中学习任务并行时的耦合效应（即协同系数），采用马尔可夫链蒙特卡洛方法推断其后验分布。这些协同项能动态调整计划的名义时长，反映操作员在场的影响。仿真与实验表明，该方法显著减少人机无意义干扰，增加人机距离，并实现最高达18%的流程执行时间降低。
原文摘要 · Abstract (English)
Collaborative robotics cells leverage heterogeneous agents to provide agile production solutions. Effective coordination is essential to prevent inefficiencies and risks for human operators working alongside robots. This paper proposes a human-aware task allocation and scheduling model based on Mixed Integer Nonlinear Programming to optimize efficiency and safety starting from task planning stages. The approach exploits synergies that encode the coupling effects between pairs of tasks executed in parallel by the agents, arising from the safety constraints imposed on robot agents. These terms are learned from previous executions using a Bayesian estimation; the inference of the posterior probability distribution of the synergy coefficients is performed using the Markov Chain Monte Carlo method. The synergy enhances task planning by adapting the nominal duration of the plan according to the effect of the operator's presence. Simulations and experimental results demonstrate that the proposed method produces improved human-aware task plans, reducing unuseful interference between agents, increasing human-robot distance, and achieving up to an 18\% reduction in process execution time.
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