In this paper, a novel human-robot collaborative framework for mixed case palletizing is presented. The framework 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.
The objective of this paper is to create a new collaborative robotic system that subsumes the advantages of mobile manipulators and supernumerary limbs. By exploiting the reconfiguration potential of a MObile Collaborative robot Assistant (MOCA), we create a collaborative robot that can function autonomously, in close proximity to humans, or be physically coupled to the human counterpart as a supernumerary body (MOCA-MAN). Through an admittance interface and a hand gesture recognition system, the controller can give higher priority to the mobile base (e.g., for long distance cocarrying tasks) or the arm movements (e.g., for manipulating tools), when performing conjoined actions. The resulting system has a high potential not only to reduce waste associated with the equipment waiting and setup times, but also to mitigate the human effort when performing heavy or prolonged manipulation tasks. The performance of the proposed system, i.e., MOCA-MAN, is evaluated by multiple subjects in two different use-case scenarios, which require large mobility or close-proximity manipulation.
This work presents a unified approach for hybrid motion control of the MObile Collaborative Robotic Assistant (MOCA). The objective is to develop a loco-manipulation controller, enabling various couplings of the arm and the mobile base movements, and particularly their purely decoupled motions. The proposed method is based on a weighted whole-body Cartesian impedance controller, where the decoupling of the motions can be achieved by solving the local optimization problem of the weighted joint torques in the first task space and its nullspace, respectively. Under this control framework, by tuning the weighting terms and a nullspace gain, three motion modes, i.e. Locomotion, Manipulation, and Modified Loco-Manipulation, are implemented. To evaluate the proposed approach, a door opening task that requires different mobility patterns of the arm, the mobile base, and their coupled movements is demonstrated. The experiment results validate the proposed methodology and provide a comprehensive understanding of the differences among the above motion modes.
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