While multi-robot cells are being used more often in industry, the problem of work-piece position optimization is still solved using heuristics and the human experience and, in most industrial cases, even a feasible solution takes a considerable amount of trials to be found. Indeed, the optimization of a generic performance index along a path is complex, due to the dimension of the feasible-configuration space. This work faces this challenge by proposing an iterative layered-optimization method that integrates a Whale Optimization and an Ant Colony Optimization algorithm, the method allows the optimization of a user-defined objective function, along a working path, in order to achieve a quasi-optimal, collision free solution in the feasible-configuration space.

尽管多机器人单元在工业中越来越多地使用，但工件位置优化的问题仍然可以通过试探法和人类经验来解决，在大多数工业情况下，即使是可行的解决方案也需要进行大量的试验才能找到。实际上，由于可行配置空间的尺寸，沿路径的通用性能指标的优化非常复杂。这项工作通过提出一种将鲸鱼优化和蚁群优化算法集成在一起的迭代分层优化方法来面对这一挑战，该方法允许沿着工作路径对用户定义的目标函数进行优化，以实现准目标。可行配置空间中的最佳无冲突解决方案。