With the development of industrial automation and artificial intelligence, robotic systems are developing into an essential part of factory production, and the human-robot collaboration (HRC) becomes a new trend in the industrial field. In our previous work, ten dynamic gestures have been designed for communication between a human worker and a robot in manufacturing scenarios, and a dynamic gesture recognition model based on Convolutional Neural Networks (CNN) has been developed. Based on the model, this study aims to design and develop a new real-time HRC system based on multi-threading method and the CNN. This system enables the real-time interaction between a human worker and a robotic arm based on dynamic gestures. Firstly, a multi-threading architecture is constructed for high-speed operation and fast response while schedule more than one task at the same time. Next, A real-time dynamic gesture recognition algorithm is developed, where a human worker’s behavior and motion are continuously monitored and captured, and motion history images (MHIs) are generated in real-time. The generation of the MHIs and their identification using the classification model are synchronously accomplished. If a designated dynamic gesture is detected, it is immediately transmitted to the robotic arm to conduct a real-time response. A Graphic User Interface (GUI) for the integration of the proposed HRC system is developed for the visualization of the real-time motion history and classification results of the gesture identification. A series of actual collaboration experiments are carried out between a human worker and a six-degree-of-freedom (6 DOF) Comau industrial robot, and the experimental results show the feasibility and robustness of the proposed system.
How to be Helpful? Supportive Behaviors and Personalization for Human-Robot Collaboration
The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in recent years. Thanks in part to advances in control and perception algorithms, robots have started to work in increasingly unstructured environments, where they operate side by side with humans to achieve shared tasks. However, little progress has been made toward the development of systems that are truly effective in supporting the human, proactive in their collaboration, and that can autonomously take care of part of the task. In this work, we present a collaborative system capable of assisting a human worker despite limited manipulation capabilities, incomplete model of the task, and partial observability of the environment. Our framework leverages information from a high-level, hierarchical model that is shared between the human and robot and that enables transparent synchronization between the peers and mutual understanding of each other’s plan. More precisely, we firstly derive a partially observable Markov model from the high-level task representation; we then use an online Monte-Carlo solver to compute a short-horizon robot-executable plan. The resulting policy is capable of interactive replanning on-the-fly, dynamic error recovery, and identification of hidden user preferences. We demonstrate that the system is capable of robustly providing support to the human in a realistic furniture construction task.
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- NSF-PAR ID:
- 10354178
- Date Published:
- Journal Name:
- Frontiers in Robotics and AI
- Volume:
- 8
- ISSN:
- 2296-9144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Robots are increasingly being employed for diverse applications where they must work and coexist with humans. The trust in human–robot collaboration (HRC) is a critical aspect of any shared-task performance for both the human and the robot. The study of a human-trusting robot has been investigated by numerous researchers. However, a robot-trusting human, which is also a significant issue in HRC, is seldom explored in the field of robotics. Motivated by this gap, we propose a novel trust-assist framework for human–robot co-carry tasks in this study. This framework allows the robot to determine a trust level for its human co-carry partner. The calculations of this trust level are based on human motions, past interactions between the human–robot pair, and the human’s current performance in the co-carry task. The trust level between the human and the robot is evaluated dynamically throughout the collaborative task, and this allows the trust to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Additionally, the proposed framework can enable the robot to generate and perform assisting movements to follow human-carrying motions and paces when the human is considered trustworthy in the co-carry task. The results of our experiments suggest that the robot effectively assists the human in real-world collaborative tasks through the proposed trust-assist framework.more » « less
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We investigate how robotic camera systems can offer new capabilities to computer-supported cooperative work through the design, development, and evaluation of a prototype system called Periscope. With Periscope, a local worker completes manipulation tasks with guidance from a remote helper who observes the workspace through a camera mounted on a semi-autonomous robotic arm that is co-located with the worker. Our key insight is that the helper, the worker, and the robot should all share responsibility of the camera view-an approach we call shared camera control. Using this approach, we present a set of modes that distribute the control of the camera between the human collaborators and the autonomous robot depending on task needs. We demonstrate the system's utility and the promise of shared camera control through a preliminary study where 12 dyads collaboratively worked on assembly tasks. Finally, we discuss design and research implications of our work for future robotic camera systems that facilitate remote collaboration.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frobt.2021.725780
