Despite the significant progress made in making robots more intelligent and autonomous, today, teleoperation remains a dominant robot control paradigm for the execution of complex and highly unpredictable tasks. Attempts have been made to make teleoperation systems stable, easy to use, and efficient in terms of physical interactions between the follower remote robot and the environment. In particular, the emergence of torque-controlled robots has permitted to regulate the interaction forces from a distance through direct force or impedance control, enabling them to engage in complex interaction tasks. Exploiting this feature, the concept of teleimpedance control was introduced as an alternative method to bilateral force-reflecting teleoperation. The aim was to create a feed-froward yet contact-efficient teleoperation by enriching the leader commands with desired impedance profiles while executing a task. Since then, the teleimpedance concept has found its way into a wide range of interface and controller designs, as well as application domains. Accordingly, after a decade of research progress, this survey aims to provide: first, a convenient introduction of the concept to new researchers in the field, second, consolidate the existing state-of-the-art for active researchers, third, and discuss the pros and cons of different methods in terms of interface and force feedback to provide guidelines for different applications and future developments.

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In this work, we present a novel control approach to human-robot collaboration that takes into account ergonomic aspects of the human co-worker during power tool operations. The method is primarily based on estimating and reducing the overloading torques in the human joints that are induced by the manipulated external load. The human overloading joint torques are estimated and monitored using a whole-body dynamic state model. The appropriate robot motion that brings the human into the suitable ergonomic working configuration is obtained by an optimisation method that minimises the overloading joint torques. The proposed optimisation process includes several constraints, such as the human arm muscular manipulability and safety of the collaborative task, to achieve a task-relevant optimised configuration. We validated the proposed method by a user study that involved a human-robot collaboration task, where the subjects operated a polishing machine on a part that was brought to them by the collaborative robot. A statistical analysis of ten subjects as an experimental evaluation of the proposed control framework is provided to demonstrate the potential of the proposed control framework in enabling ergonomic and task-optimised human-robot collaboration.

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In this paper, we propose a method for improving the human operator's arm posture during bilateral teleoperation. The method is based on a musculoskeletal model that considers human operator's arm dynamics and the feedback force from the haptic interface (master), which is used to control a robotic arm (slave) in a remote environment. We perform an online optimisation to find the optimal configuration that has the longest endurance time with respect to muscle fatigue. Next, a trajectory is generated on the haptic interface in order to guide the human arm into the optimal configuration. The teleoperation is temporarily suspended by decoupling the master from the slave robot when the haptic device is being reconfigured. Afterwards, the loop is coupled again and the slave robot is controlled from the position where it stopped after the haptic interface guided the operator's arm to the optimised configuration. The main advantage of the proposed method is that the human operator can perform the task with less effort, which increases the endurance time. To validate our approach, we performed proof-of-concept experiments on a teleoperation system composed of two Franka Emika robots, where one was serving as master and the other as slave.

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In this paper, we propose a method for selective monitoring and management of human muscle fatigue in human-robot co-manipulation scenarios. The proposed approach uses a machine learning technique to learn the complex relationship between individual human muscle forces, arm configuration and arm endpoint force that are provided by a sophisticated offline musculoskeletal model. The estimated muscle forces are used in the fatigue model to estimate the individual muscle fatigue levels online. Two fatigue management protocols are proposed that enable the robot to handle and reduce the human fatigue by altering the configuration of task execution. The first protocol uses optimisation technique to find the optimal position for task execution, where the fatigue-related endurance time can be maximised. The second protocol divides the arm muscles into groups and then alters the direction of endpoint force so that the fatigued muscle group can relax and the relaxed muscle group becomes active. The proposed method has a potential to enable the robot to facilitate safer and more ergonomic working conditions for the human coworker. The main advantage of this approach is that it can operate online, and that all the measurements can be performed by the robot sensory system, which can significantly increase the applicability in real world scenarios. To validate the proposed method, we performed multiple experiments with two collaborative tasks (polishing and drilling) under different conditions.

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The deployment of industrial robotic cells based on lean manufacturing principles enables the development of fast-reconfigurable assembly lines in which human and robotic agents collaborate to achieve a shared task. To ensure the effective coordination of the shared effort, each task must be decomposed into a sequence of atomic actions that can be assigned either to a single agent or to the combination of more agents, according to a defined metric. While task allocation is a general problem and has been discussed intensively in other fields, less effort has been devoted in industrial scenarios, involving mixed human–robot teams and in particular, to the factors that should be considered in allocating tasks among a heterogeneous set of agents in collaborative manufacturing scenarios. In this letter, we investigate the agent characteristics that should be considered in the task allocation problem of fast-reconfigurable systems in industrial assembly processes. First, we introduce a set of indices, namely task complexity, agent dexterity, and agent effort, to evaluate agent performance with respect to a task. Second, we propose an offline allocation algorithm that combines the performance indices to assign optimally the task to the team agents. Finally, we validate the framework in a proof-of-concept collaborative assembly of a metallic structure. The results show that the workload is shared through the agents according to their particular physical capabilities and skill levels. A subjective analysis of the proposed collaborative framework on 12 healthy participants also validated the intuitiveness-of-use and improved performance.@en