Towards Optimal Task Positioning In Multi-Robot Cells, Using Nested Meta-Heuristic Swarm Algorithms

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.