Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design

By Scott A. Green, Mark Billinghurst, Xiaoqi Chen, J. Geoffrey Chase. 2008

340 citations recorded by the source corpus.

NASA's vision for space exploration stresses the cultivation of human-robotic systems. Similar systems are also envisaged for a variety of hazardous earthbound applications such as urban search and rescue. Recent research has pointed out that to reduce human workload, costs, fatigue driven error and risk, intelligent robotic systems will need to be a significant part of mission design. However, little attention has been paid to joint human-robot teams. Making human-robot collaboration natural and efficient is crucial. In particular, grounding, situational awareness, a common frame of reference and spatial referencing are vital in effective communication and collaboration. Augmented Reality (AR), the overlaying of computer graphics onto the real worldview, can provide the necessary means for a human-robotic system to fulfill these requirements for effective collaboration. This article reviews the field of human-robot interaction and augmented reality, investigates the potential avenues for creating natural human-robot collaboration through spatial dialogue utilizing AR and proposes a holistic architectural design for human-robot collaboration.

Subfields: Computer Vision and Pattern Recognition, Mechanical Engineering, Social Psychology

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Citations in the corpus

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “As an integral component of reasoning systems, approaches to construct human representations have been widely used in a variety of real-world applications, including video analysis [1], surveillance [2], robotics [3], humanmachine interaction [4], augmented and virtual reality [5], assistive living ”
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, positive): “However, with recent developments in many robot capabilities such as sensing, perception, control, teaching, and learning the very nature of the human – robot relationship is being examined with a focus toward attaining greater productivity, ﬂ exibility, and adaptability [69,70].”
Shared Autonomy via Deep Reinforcement Learning (2018, positive): “We build on shared autonomy work in which the system is initially unaware of the user’s goal [9, 7, 21, 13, 24, 16, 10] and explore problem statements with unknown dynamics, unknown user policy, and unknown goal representation.”
Recipient design in human communication: simple heuristics or perspective taking? (2012, background): “For example, it can give us insight into how to create artificial agents that can communicate in human ways (e.g., in the context of human-robot interaction; Breazeal, 2002; Green et al., 2008).”
Progress and prospects of the human–robot collaboration (2017, positive): “A solution to this issue can be obtained by the introduction of shared communication modalities [Green et al., 2008,Lackey et al., 2011,Lackey et al., 2011].”
Shared autonomy via hindsight optimization for teleoperation and teaming (2017, methodology): “Though we cannot conﬁdently predict their single goal, our method starts making progress for the other boxes. leads to ineﬀective collaboration (Vanhooydonck et al., 2003; Goodrich and Jr., 2003; Green et al., 2007).”
Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues (2014, background): “The necessity for more powerful intelligent agents that can interact with human operators in increasingly sophisticated ways will require that current capabilities be augmented with techniques and technologies that facilitate effective H-A team interactions [24], [47].”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “We refer to several review articles on the state-of-the-arts in engineeing and design [508]–[512], medicine [513]–[517] and education [518]–[521].”
Gesture recognition for human-robot collaboration: A review (2017, background): “Literature suggested that human workers have unique problem-solving skills and sensory-motor capabilities, but are restricted in force and precision (Krüger et al., 2009; Green et al., 2008).”