Sheet metal manufacturing is finding wide applications in automotive and aerospace industries. Handling of giant sheet materials in manufacturing industries is one of the key problems. Utilization of robots, viz SwarmItFIX, will address this problem and automate the fixturing process, which greatly reduces lead time and thus the production cost. Implementation of intelligence into the robots will further improve efficiency in handling and reduce manufacturing inaccuracies. In this work, two different novel planners are proposed which do path planning for the heads of the SwarmItFIX agents. The environment of the problem is modeled as a Markov Decision Problem. The first planner uses the Value Iteration and Policy Iteration (PI) algorithms individually and the second planner performs the Monte Carlo control reinforcement learning. Finally, when the simulation is done and parameters of the proposed three algorithms along with existing Constraint Satisfaction Problem algorithm are compared with each other. It is observed that the proposed PI algorithm returns the plan much faster than the other algorithms. In the near future, the efficient planning model will be tested and implemented into the SwarmItFIX setup at the PMAR laboratory, University of Genoa, Italy.

钣金制造正在汽车和航空航天行业中找到广泛的应用。制造业中巨大板材的处理是关键问题之一。机器人的使用，即SwarmItFIX，将解决此问题并使装夹过程自动化，从而大大缩短了交货时间，从而降低了生产成本。在机器人中实施智能将进一步提高处理效率并减少制造不准确之处。在这项工作中，提出了两种不同的新颖计划器，它们为SwarmItFIX代理程序的头部进行路径计划。问题的环境被建模为马尔可夫决策问题。第一个计划者单独使用值迭代和策略迭代（PI）算法，第二个计划者执行蒙特卡洛控制强化学习。最后，在进行仿真时，将所提出的三种算法的参数与现有的约束满足问题算法进行比较。可以看出，提出的PI算法比其他算法更快地返回了计划。在不久的将来，将在意大利热那亚大学PMAR实验室的SwarmItFIX装置中测试并实施有效的计划模型。