Research Update by PRO and ICCS: Intricacies of human-robot collaboration (HRC) within industrial settings
This blog entry explores the intricacies of human-robot collaboration (HRC) within industrial settings, with a focus on achieving seamless interaction through a skill-based robot task execution engine.
The FELICE proposed framework intricately orchestrates the dynamic interplay between the perception and acting/reacting layers in the context of human-robot collaboration, with a specific emphasis on the role of Behavior Trees (BTs) in both task execution and deviation handling.
Behavior Trees (BTs) are increasingly favored for human-robot collaborative assembly tasks in industrial settings due to their hierarchical, modular structure [1]. BTs excel in managing complex assembly scenarios by breaking tasks into manageable sub-tasks, promoting fault tolerance, and ensuring real-time adaptability. Their organized architecture facilitates skill mapping and organization, enhancing efficiency in skill-based robot task execution. The use of sub-trees and fallback sub-trees in BT design promotes modularity. This structured approach allows task de-composition and modularity, catering to collaborative assembly intricacies and enabling real-time adaptability. Fig. 1 demonstrates a BT structure for a pickup skill.
![](https://www.felice-project.eu/wp-content/uploads/2024/04/image3.png)
In FELICE the Behavior Tree-based Task Execution (BTE) is seamlessly integrated into the acting/reacting layer, collaborating synergistically with the perception layer, which includes robot proprioception and object localization modules as shown in Fig. 2.
![](https://www.felice-project.eu/wp-content/uploads/2024/04/image4.png)
This integration facilitates the efficient detection and handling of deviations, particularly those arising from failures during robotic manipulation (grasping), ensuring real-time responsiveness to dynamic changes in collaborative assembly tasks. A dynamic change could be either a failure of robot localization or a poor object detection result. Failures, in terms of BTs, are implemented as a Dynamic Behavior Tree Execution Engine (DBTE) which loads a BT as a two-stage approach (Fig. 3).
![](https://www.felice-project.eu/wp-content/uploads/2024/04/image5.png)
n a real case scenario on the shopfloor, initially, the robot arm moves to a scan pose relative to itself, allowing scanning from the side of the tool/object. Subsequently, the arm moves to a scanning pose relative to the tool/object, enabling scanning from the top. At each iteration of moving to a scanning position and performing a scan, there is an interaction between the RAE and the ODL module.
[1] 11. M. Iovino, E. Scukins, J. Styrud, P. Oegren, and C. Smith, “Asurvey of behavior trees in robotics and ai,” Robotics and Autonomous Systems, 2022, Vol. 154, pp. 104096