Towards optimal task positioning in multi-robot cells, using nested meta-heuristic swarm algorithms

• Published in: Robotics and computer-integrated manufacturing Vol. 71; p. 102131
• Main Authors: Mutti, S., Nicola, G., Beschi, M., Pedrocchi, N., Tosatti, L. Molinari
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2021; Elsevier BV
• Subjects: Algorithms; Ant colony optimization; Collision avoidance; Configurations; Heuristic methods; Industrial robotics; Motion planning; Multi-robot coordination; Multiple robots; Optimization; Optimization algorithms; Performance indices; Workpieces; Motion planning; Multi-robot coordination; Optimization algorithms; Industrial robotics
• ISSN: 0736-5845; 1879-2537
• DOI: 10.1016/j.rcim.2021.102131

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. •Work-piece position affects the outcome of the processing in a robotic operated cell.•Heuristic algorithms are shown to explore consistently the optimization search space.•Task redundancy exploitation is a key factor in robotic operated cells.