Conference paper Open Access

Towards an Intelligent Collaborative Robotic System for Mixed Case Palletizing

Lamon, Edoardo; Leonori, Mattia; Kim, Wansoo; Ajoudani, Arash

In this paper, a novel human-robot collaborative framework for mixed case palletizing is presented. The frame-work addresses several challenges associated with the detection and localisation of boxes and pallets through visual perception algorithms, high-level optimisation of the collaborative effort through effective role-allocation principles, and maximisation of packing density. A graphical user interface (GUI) is additionally developed to ensure an intuitive allocation of roles and the optimal placement of the boxes on target pallets. The framework is evaluated in two conditions where humans operate with and without the support of a Mobile COllaborative robotic Assistant(MOCA). The results show that the optimised placement can improve up to the 20% with respect to a manual execution of the same task, and reveal the high potential of MOCA in increasing the performance of collaborative palletizing tasks.

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