串并联双机器人系统任务分配的蚁群优化

Task Assignment for Serial and Parallel Dual-robot System via Ant Colony Optimization

针对一种新型串并联双机器人联合作业系统的任务分配进行方法设计和整体优化。分别采用蚁群优化中的近似非确定性树搜索(Approximate nondeterministic tree search,ANTS)和最大最小蚂蚁系统(Max-min-ant-system,MMAS)作为任务分配的优化策略,并在MMAS中加入局部搜索以进一步优化路径构建过程中得到的局部最优解。仿真结果以及与之前相关研究成果的对比表明,MMAS在寻优过程中的迭代收敛速度优于ANTS,且经过一段时间的开发探索之后,获得的最优解的质量也比ANTS要好;MMAS与局部搜索相结合的方法比单独使用MMAS更加进一步提高了最终解的质量。进化曲线证明了算法对系统任务分配及优化的适应性和优越性。试验结果经与传统组合优化方法对比,进一步验证了算法的优化效果。

Method design and overall optimization of task assignment for a new type of serial and parallel dual-robot associated processing system are carried out.Two algorithms of ant colony optimization,the approximate nondeterministic tree search（ANTS） and the max-min-ant-system（MMAS） are used to be the optimization methods for task assignment.Local search is adopted in MMAS in order to get better local-best-solution from the path construction process.Simulation results and their comparison with the previous relevant study results show that the iteration convergence speed of MMAS in the optimizing process is faster than ANTS,and the quality of the final optimal solution obtained after a period of time for exploration is also better than ANTS.The combination of MMAS and local search further improves the quality of the final solution than using MMAS only.Evolution curves demonstrate the adaptability and superiority of the algorithms for task assignment and optimization of the system.Experiment results further validate the effect of optimization in comparison with traditional combinatorial optimization method.