This letter proposes a novel human–robot collaboration (HRC) control approach to alert and reduce the static joint torque overloading of a human partner while executing shared tasks with a robot. Using a preidentified statically equivalent serial chain model, variations of the centre-of-pressure and ground reaction force are calculated, and the overloading joint torques are evaluated in real time to initially alert the human about consequent injuries. An online optimization technique is implemented to adjust the robot trajectories facilitating the human to achieve more ergonomic body poses throughout the HRC task. The optimized human configurations are calculated by taking into account the human stability, the robot, and the human workspaces, and the task constraints, and illustrated to the human in real time. The experimental evaluation of the proposed technique is achieved in a human–robot load sharing task as a representative example of coassembly or transportation scenarios in industrial settings.

This letter presents a novel teleoperation interface that enables remote loco-manipulation control of a MObile Collaborative robotic Assistant (MOCA). MOCA is a new research platform developed at the Istituto Italiano di Tecnologia (IIT), which is composed of a lightweight manipulator arm, a Pisa/IIT SoftHand, and a mobile platform driven by four omni-directional wheels. A whole-body impedance controller is consequently developed to ensure accurate tracking of the impedance and position trajectories at MOCA end-effector by considering the causal interactions in such a dynamic system. The proposed teleoperation interface provides the user with two control modes: locomotion and manipulation. The locomotion mode receives inputs from a personalized human center-of-pressure model, which enables real-time navigation of the MOCA mobile base in the environment. The manipulation mode receives inputs from a tele-impedance interface, which tracks human arm endpoint stiffness and trajectory profiles in real time and replicates them using the MOCA's whole-body impedance controller. To evaluate the performance of the proposed teleoperation interface in the execution of remote tasks with dynamic uncertainties, a sequence of challenging actions, i.e., navigation, door opening, and wall drilling, has been considered in the experimental setup.

In this paper, we propose a novel method for the control of human-robot eo-manipulation that takes into aeeount the ergonomie requirements for the human eo-worker. The robot uses a whole-body dynamic model of the human to optimise for the position of the eo-manipulation task in the workspace. In this configuration, the overloading joint torques, i.e. the effects of an external load in human body joints, are minimised. In addition, the optimisation process ineludes several constraints, sueh as human arm manipulability properties, to ensure that the human has a good manipulation eapaeity in the given eonfiguration. The main advantage of this approaeh is that the robot ean potentially help to reduee the work-related strain and increase the productivity of the human eo-worker. In addition, the on-line estimation of the overloading joint torques does not require an external foree plate or sensor insoles. We validated the proposed method with experiments in two eo-manipulation tasks: human using a device to polish an object that is delivered by the robot and a human-robot object handover. The results show that the proposed method is successful in achieving the ergonomie conditions for the human during the eollaboration.