Sensor-based Human-Robot Collaboration
Name of demonstration
Sensor-based Human-Robot Collaboration
Main objective
Demonstrating capabilities for vision-based collaboration between human and robot. Deep learning-based perception tools are utilized to provide input for the collaboration.
Short description
Demonstration of a vision-based system for human-robot collaboration in the assembly of diesel engine components. Visual perception of a person, their actions is utilized for coordinating the shared task. Visual perception is also used to detect objects and targets and perform grasping actions for pick and placement, as well as robot-human hand-overs.Â
Owner of the demonstrator
Tampere University
Responsible person
Roel Pieters roel.pieters@tuni.fi
NACE
C29.3 - Manufacture of parts and accessories for motor vehicles
Keywords
Robotics, Vision System, Machine Learning, human-robot collaboration, cobot assisted manufacturing, AI.
Benefits for the users
Collaboration between humans and industrial robots can relieve humans from tedious tasks that are more suited for robot execution. Vision is a convenient modality and interface for interaction/coordination as it does not require any contact and can be activated from a distance. This use-case demonstrates capabilities for vision-based collaboration between human and robot, by visual perception tools such as human skeleton detection, human action recognition and the detection and pose estimation of objects and targets in the scene.
Innovation
Perception and situational awareness of robot systems can be enhanced, such that fluent and responsive collaboration between human and robot is possible. Perception models, based on deep learning, are ideal for this, as they can be accurate, reliable and fast to execute.
Risks and limitations
Software Malfunction: As the whole system is operates by a computer, there are chances that software and connections through devices get malfunctioned. These can endanger the operator’s safety while the system is not stopped. As a result, it requires a back-up safety system running all the time and malfunctioning of this system should result in protective stop of robot system. Environmental disturbances: lighting conditions, dust electrical interferences can affect the detection accuracy of the visual tools, which can cause false or mis-detections.
Technology readiness level
5
Sectors of application
Manufacturing .
Potential sectors of application
Any sector where collaboration between human and robot would occur, e.g., inspection and maintenance, agrofood, healthcare
Patents / Licenses / Copyrights
Hardware / Software
Hardware:
Franka Emika collaborative robot Standard RGB camera (e.g. Intel Realsense D435) Workstation with or without GPU running Linux Ubuntu 20.04 LTS
Software:
Linux Ubuntu 20.04 LTS ROS1 Noetic or ROS2 Foxy OpenDR toolkit will install all required dependencies: https://github.com/opendr-eu/opendr/
Human Skeleton Detection
Vision-based human skeleton detection tool for detecting a person, and their skeleton nodes in the field of...
LEARN MORE
Object and target detection
Vision-based object and target detection tool from RGB images. The tool requires a dataset for training the...
LEARN MORE
Robot vision-based object grasping
Object grasping from RGB images with the object and target detection tool, executed by a collaborative robo...
LEARN MORE
Skeleton based Human action recognition
Skeleton-based human action recognition tool for recognizing human actions, in the field of view, from a de...
LEARN MORE