Abstract
The emergence of collaborative robotics has had a great impact on the development of robotic solutions for cooperative tasks nowadays carried out by humans, especially in industrial environments where robots can act as assistants to operators. Even so, the coordinated manipulation of large parts between robots and humans gives rise to many technical challenges, ranging from the coordination of both robotic arms to the human–robot information exchange. This paper presents a novel architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop, as well as adding mechanisms to provide effective robot-to-human feedback for a successful and satisfactory task completion. The obtained results demonstrate the validity of the proposed architecture as well as its suitability for the implementation of collaborative robotic systems.
Highlights
The emergence of collaborative robotics changed the development of robotic solutions drastically for cooperative tasks
Industrial environments offer an interesting scenario for collaborative robotics, an environment where robots could act as assistants to operators [1,2], helping them in their usual tasks
Su et al [9] propose the use of a recurrent neural network (RNN) to perform the trajectory control of redundant robot manipulators
Summary
The emergence of collaborative robotics changed the development of robotic solutions drastically for cooperative tasks. Two operators are able to transport and place large parts with few or no visual information of their partner, using mainly the feedback of the forces sensed during the manipulation to adapt their trajectories and fulfill the task, adding extra information only when required. Even so, it is important choosing the most suitable cues for this information exchange to ensure successful completion of the task. The presented architecture tackles these three elements, defining a new scheme for dual arm co-manipulation tasks.
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