Unsupervised Learning of Assistive Camera Views by an Aerial Co-robot in Augmented Reality Multitasking Environments

Bentz, William; Dhanjal, Sahib Singh; Panagou, Dimitra

doi:10.1109/icra.2019.8793587

Cited by 9 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in Bentz et al (2019) implemented a system in which an aerial collaborative robot feeds the data from the head motions of a human performing a multitasking job to an Expectation-Maximization that learns which environment views have the highest visual interest to the user. Consequently, the co-robot is directed to capture these relevant views through its camera, and an AR HMD supplements the human’s field of view with views when needed.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Bassyouni

Elhajj

2021

Front. Robot. AI

View full text Add to dashboard Cite

Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user’s situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Multitasking is improved in Bentz et al (2019) , where data from a HMD are fit to a model that identifies views of interest to the human, directs an aerial co-robot to capture these views, and augments them on his/her. The input data is the head pose collected through a VICON motion capture system.…”

Section: Discussionmentioning

confidence: 99%

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Bassyouni

Elhajj

2021

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…Internal Status [479] Robot's Capability [66] Object Status [21] Sensor/Camera Data [377] Plan and Target [136] Simulation [76] Interactive Content [386] Virtual Background [8] [ 13,15,18,19,66,86,98,101,124,158,195,262,282,285,302,324,332,375,474,479,481] [4, 15,21,24,38,47,79,84,89,102,114,143,149,150,153,171,174,178,182,184,185,201,216,2...…”

Section: Internal Information External Information Plan and Activity ...mentioning

confidence: 99%

“…The most common approaches for technical evaluation are measuring latency [48,51,59,69,318,495], accuracy of tracking [15,58,59,64,486], and success rate [262,314]. Also, we found some works evaluate their system performances based on the comparison with other systems, which for example, include comparing tracking algorithms with other approaches [38,59,63,132,406].…”

Section: Evaluation Strategiesmentioning

confidence: 99%

Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces

Suzuki,

Karim,

Xia

et al. 2022

Preprint

View full text Add to dashboard Cite

“…Zhou et al designed a flying drone that could keep tracking a human motion using a normal color camera [121]. Bentz et al presented an assistive aerial robot that could observe regions most interesting to the human and broadcast these views to the humans augmented reality display [7]. This resulted in reduced head motions of the human as well as improved reaction time.…”

Section: Human Following Uavsmentioning

confidence: 99%

Companion Unmanned Aerial Vehicles: A Survey

Liew¹,

Yairi²

2020

Preprint

View full text Add to dashboard Cite

Recent technological advancements in smallscale unmanned aerial vehicles (UAVs) have led to the development of companion UAVs. Similar to conventional companion robots, companion UAVs have the potential to assist us in our daily lives and to help alleviating social loneliness issue. In contrast to ground companion robots, companion UAVs have the capability to fly and possess unique interaction characteristics. Our goals in this work are to have a bird's-eye view of the companion UAV works and to identify lessons learned and guidelines for the design of companion UAVs. We tackle two major challenges towards these goals, where we first use a coordinated way to gather topquality human-drone interaction (HDI) papers from three sources, and then propose to use a perceptual map of UAVs to summarize current research efforts in HDI. While being simple, the proposed perceptual map can cover the efforts have been made to realize companion UAVs in a comprehensive manner and lead our discussion coherently. We also discuss patterns we noticed in the literature and some lessons learned throughout the review. In addition, we recommend several areas that are worth exploring and suggest a few guidelines to enhance HDI researches with companion UAVs.

show abstract

Unsupervised Learning of Assistive Camera Views by an Aerial Co-robot in Augmented Reality Multitasking Environments

Cited by 9 publications

References 21 publications

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces

Companion Unmanned Aerial Vehicles: A Survey

Contact Info

Product

Resources

About