Carmichael, MG, Aldini, S, Khonasty, R, Tran, A, Reeks, C, Liu, D, Waldron, KJ & Dissanayake, G 2019, 'The ANBOT: An Intelligent Robotic Co-worker for Industrial Abrasive Blasting', IEEE International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Macau, China, pp. 8026-8033.View/Download from: Publisher's site
© 2019 IEEE. We present the ANBOT, an intelligent robotic coworker for physical human-robot collaboration. The ANBOT system assists workers performing industrial abrasive blasting, shielding them from the large forces experienced during this physically demanding task. The co-operative robotic system combines the strength and endurance of robots with the decision making of skilled workers. The inherent challenges in human-robot collaboration, combined with the difficult blasting environment required novel design decisions to be made and new solutions to be developed. These include an approach for handling kinematic singularities in a manner suitable for human-robot co-operation, estimating worker pose under poor visibility conditions, and an intuitive control scheme that adapts the robotic assistance based on the estimated strength of the worker. In this work we summarise the ANBOT system and present findings from preliminary site trials. The trials included several real industrial blasting tasks under the control of a skilled abrasive blasting worker who had no experience working alongside a robot. Results demonstrate the suitability of the ANBOT for practical industrial applications.
Singh, AK, Aldini, S, Leong, D, Wang, YK, Carmichael, MG, Liu, D & Lin, CT 2020, 'Prediction Error Negativity in Physical Human-Robot Collaboration', 8th International Winter Conference on Brain-Computer Interface, BCI 2020.View/Download from: Publisher's site
© 2020 IEEE. Cognitive conflict is a fundamental phenomenon of human cognition, particularly during interaction with the real world. Understanding and detecting cognitive conflict can help to improve interactions in a variety of applications, such as in human-robot collaboration (HRC), which involves continuously guiding the semi-autonomous robot to perform a task in given settings. There have been several works to detect cognitive conflict in HRC but without physical control settings. In this work, we have conducted the first study to explore cognitive conflict using prediction error negativity (PEN) in physical human-robot collaboration (pHRC). Our results show that there was a statistically significant (p =. 047) higher PEN for conflict condition compared to normal conditions, as well as a statistically significant difference between different levels of PEN (p =. 020). These results indicate that cognitive conflict can be detected in pHRC settings and, consequently, provide a window of opportunities to improve the interaction in pHRC.
Aldini, S, Akella, A, Singh, A, Wang, Y-K, Carmichael, M, Liu, D & Lin, C-T 2019, 'Effect of Mechanical Resistance on Cognitive Conﬂict in Physical Human-Robot Collaboration', https://ieeexplore.ieee.org/xpl/conhome/8780387/proceeding, International Conference on Robotics and Automation, IEEE, Canada, pp. 6137-6143.View/Download from: Publisher's site
Physical Human-Robot Collaboration (pHRC) is about the interaction between one or more human operator(s) and one or more robot(s) in direct contact and voluntarily exchanging forces to accomplish a common task. In any pHRC, the intuitiveness of the interaction has always been a priority, so that the operator can comfortably and safely interact with the robot. So far, the intuitiveness has always been described in a qualitative way. In this paper, we suggest an objective way to evaluate intuitiveness, known as prediction error negativity (PEN) using electroencephalogram (EEG). PEN is defined as a negative deflection in event related potential (ERP) due to cognitive conflict, as a consequence of a mismatch between perception and reality. Experimental results showed that the forces exchanged between robot and human during pHRC modulate the amplitude of PEN, representing different levels of cognitive conflict. We also found that PEN amplitude significantly decreases (p <; 0.05) when a mechanical resistance is being applied smoothly and more time in advance before an invisible obstacle, when compared to a scenario in which the resistance is applied abruptly before the obstacle. These results indicate that an earlier and smoother resistance reduces the conflict level. Consequently, this suggests that smoother changes in resistance make the interaction more intuitive.
Aldini, S, Carmichael, MG & Liu, D 2019, 'A Risk Reduction Framework for Design of Physical Human-Robot Collaboration', https://ssl.linklings.net/conferences/acra/acra2019_proceedings/views/b…, Australasian Conference on Robotics and Automation, Adelaide.
As robots designed to physically interact with humans become common in various application areas, shared workspaces and force exchange between human and robot lead to new challenges in terms of safety.
Often, a variety of safety techniques is necessary, and deciding what methods to include in a comprehensive safety framework is not an easy task. This paper is concerned with the design of robotic co-wokers that involve physical Human-Robot Collaboration (pHRC), with humans and robots in continuous direct physical contact and exchanging forces.
A hierarchical risk reduction framework is presented for guiding the design of robotic co-workers to reduce the risk associated with hazards commonly found in pHRC tasks. A case study is presented to demonstrate the use of the framework in designing an Assistance-as-Needed roBOT (ANBOT) which has been extensively tested in practical industry applications.
Carmichael, MG, Aldini, S & Liu, D 2017, 'Human user impressions of damping methods for singularity handling in human-robot collaboration', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Australia, pp. 107-113.
Kinematic singularity is a fundamental and well understood problem of robot manipulators, with many methods having been developed to ensure safe and robust operation in proximity to singularity. However little attention has been given to the scenario where the robot and human are working in physical contact to collaboratively perform a task. In such a scenario the feelings and impressions of the human operator should be considered when developing solutions for handling singularity. This work presents an experimental study comparing three modes of handling kinematic singularities with respect to the impressions of the human operator. Two of the modes are based on traditional Damped-Least-Squares. The third method uses an asymmetric damping behavior proposed as being well suited for applications involving physical human-robot interaction. The three modes are tested and compared by subjects performing a mock industrial task, and feedback from the subjects analyzed to identify the preferred mode. Results indicate that the choice of method used affects the user's impressions of the interaction, and the asymmetrical damping behavior can produce a preferred interaction experience with human operators during tasks.
Khonasty, R, Carmichael, MG, Liu, D & Aldini, S 2017, 'Effect of External Force and Bimanual Operation on Upper Limb Pose during Human-Robot Collaboration', Australasian Conference on Robotics and Automation 2017, Australasian Conference on Robotics and Automation, ARAA, Sydney Australia, pp. 1-9.
During physical Human-Robot Interaction
(pHRI) in industrial applications such as
human-robot collaborative abrasive blasting,
the operator often interacts with the robot using
two hands, exchanging forces through handle
bars. For the robot to provide appropriate
assistance to the operator and for safe interaction,
it would be beneficial for the robot
to know the pose of the user. This problem
is often challenging due to environmental factors,
limited sensing capability in the environment
and the robot, and redundancy of the human
upper-limb. This paper presents experimental
study on how two-hand interaction and
force exchange affect the operators upper-limb
pose, which can be characterized by swivel angle.
The poses of ten subjects were recorded as
they interacted with a collaborative robot. Differences
in the adopted upper limb pose were
analyzed with respect to factors such as unimanual
versus bimanual operation, and the amplitude
of interaction force between an operator
and the robot. The results discovered that the
the effect of bimanual operation on the upper
limb pose differs between individuals and the
magnitude of the force had a varying effect on
the pose. The requirement of applying a force
forward produced an overall lower swivel angle