基于栅格理论的智能机器人多任务协同管理模型设计

Design of Multitask Collaborative Management Model for Intelligent Robots Based on Grid Theory

为了实现多任务协同管理,提高机器人的智能化水平和任务完成效率,研究首先通过结合系统任务状态与目标两方面得到目标任务状态,并在栅格理论下管理不同任务元。随后利用改进人工蜂群算法对粒子群算法进行优化,再将其应用于多任务协同管理中。结果表明,所提方法的收敛精度在迭代300次时趋于平稳,平均适应度始终维持在100到140之间。在多任务协同管理中,该方法所得效率提升度可达8%,并且仅耗费14 s。说明研究提出的多任务协同管理方法能够在较短的时间内实现较高的协同度,并且运行效率较高,所需时间更短,可以有效保证智能机器人协同工作系统的高效运行,为智能制造的进一步优化提供了技术参考。

Traditional sensor management methods are difficult to meet the needs of intelligent robot systems for multitasking collaborative work.Therefore,research has been conducted to combine the system task state with the target to obtain the target task state,utilize grid theory to achieve the management of different task elements,and optimize the Particle Swarm Optimization(PSO) algorithm by combining the improved Artificial Bee Colony Algorithm(ABC) algorithm,Finally,it is applied to the goal optimization of multi task collaborative management.The results show that the convergence accuracy of the proposed method tends to be stable during 300 iterations,and the average fitness is always maintained between 100 and 140.In multi task collaborative management,the efficiency improvement achieved by this method can reach 8%,and only takes 14 seconds.This indicates that the proposed multi task collaborative management method can achieve a high degree of collaboration in a relatively short time,with higher operational efficiency and shorter time required.It can effectively ensure the efficient operation of the intelligent robot collaborative work system,providing a technical reference for further optimization of intelligent manufacturing.