Özbaltan Mete, Özbaltan Nihan, Bıçakcı Yeşilkaya Hazal Su, Demir Murat, Şeker Cihat, Yıldırım Merve
Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, İzmir Bakırçay University, 35665 İzmir, Türkiye.
Department of Computer Engineering, Faculty of Engineering and Architecture, İzmir Bakırçay University, 35665 İzmir, Türkiye.
Biomimetics (Basel). 2025 May 24;10(6):346. doi: 10.3390/biomimetics10060346.
Task scheduling for multiple humanoid robot manipulators in industrial and collaborative settings remains a significant challenge due to the complexity of coordination, resource sharing, and real-time decision-making. In this study, we propose a framework for modeling task scheduling for multiple humanoid robot manipulators by using the symbolic discrete controller synthesis technique. We encode the task scheduling problem as discrete events using parallel synchronous dataflow equations and apply our synthesis algorithms to manage the task scheduling of multiple humanoid robots via the resulting controller. The control objectives encompass the fundamental behaviors of the system, strict rules, and mutual exclusions over shared resources, categorized as the safety property, whereas the optimization objectives are directed toward maximizing the throughput of robot-processed products with optimal efficiency. The humanoid robots considered in this study consist of two pairs of six-degree-of-freedom (6-DOF) robot manipulators, and the inverse kinematics problem of the 6-DOF arms is addressed using metaheuristic approaches inspired by biomimetic principles. Our approach is experimentally validated, and the results demonstrate high accuracy and performance compared to other approaches reported in the literature. Our approach achieved an average efficiency improvement of 40% in 70-robot systems, 20% in 30-robot systems, and 10% in 10-robot systems in terms of production throughput compared to systems without a controller.
由于协调、资源共享和实时决策的复杂性，在工业和协作环境中为多个人形机器人操纵器进行任务调度仍然是一项重大挑战。在本研究中，我们提出了一个框架，通过使用符号离散控制器综合技术对多个人形机器人操纵器的任务调度进行建模。我们使用并行同步数据流方程将任务调度问题编码为离散事件，并应用我们的综合算法通过生成的控制器来管理多个人形机器人的任务调度。控制目标包括系统的基本行为、严格规则以及对共享资源的互斥，这些被归类为安全属性，而优化目标则旨在以最佳效率最大化机器人加工产品的吞吐量。本研究中考虑的人形机器人由两对六自由度（6-DOF）机器人操纵器组成，并且使用受仿生原理启发的元启发式方法解决了6-DOF手臂的逆运动学问题。我们的方法经过了实验验证，结果表明与文献中报道的其他方法相比，具有更高的准确性和性能。与没有控制器的系统相比，我们的方法在70个机器人的系统中生产吞吐量平均提高了40%，在30个机器人的系统中提高了20%，在10个机器人的系统中提高了10%。
