Monocular vision-based human following on miniature robotic blimp

Yao, Ningshi; Anaya, Emily; Tao, Qiuyang; Cho, Sung‐Jin; Zheng, Hongrui; Zhang, Fumin

doi:10.1109/icra.2017.7989369

Cited by 54 publications

(32 citation statements)

References 16 publications

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“…A notable example of an autonomous indoor blimp is also the GT-MAB [16], one of the smallest autonomous indoor LTA platforms designed for human-robot interaction and autonomy studies. In [17], the GT-MAB was demonstrated in a human following and gesture recognition scheme, paving the road for flying airship companions.…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost, Open-Source, Robotic Airship for Education and Research

Gorjup

Liarokapis

2020

IEEE Access

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Miniature indoor robotic airship platforms offer high mobility, safety, and extended flight times. This paper focuses on the feasibility, design, development, and evaluation of such a platform for robotics education and research. Selected commercially available envelope materials were considered and tested in terms of their helium retention capability and mechanical properties. The obtained envelope properties were used in a feasibility study, demonstrating that indoor airships are environmentally and financially viable, given an appropriate material choice. The platform's mechanical design was studied in terms of gondola placement and rotor angle positioning, resulting in an unconventional, asymmetric arrangement. The developed system was finally tested in a simple path following experiment for proof-of-concept purposes, proving its efficiency in attaining the desired heading and altitude configuration. The proposed robotic airship platform can be used for a variety of education and research oriented applications. Its design is open-source, facilitating replication by others. INDEX TERMS Airship, lighter-than-air, open educational resources, path following, unmanned aerial vehicles.

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Section: Related Workmentioning

confidence: 99%

A Low-Cost, Open-Source, Robotic Airship for Education and Research

Gorjup

Liarokapis

2020

IEEE Access

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“…8, was developed by the University of Zürich [21]. A similar platform, the Miniature Autonomous Blimp (MAB), was developed by the Georgia Institute of Technology for applications in researching control of underwater vehicles [22][23][24].…”

Section: Aerialmentioning

confidence: 99%

Survey of unmanned subterranean exploration, navigation, and localisation

Martz

Al-Sabban

Smith

2020

IET cyber-systems robotics

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Subsurface networks include mining tunnels, caves, and the urban underground. Each of these environments presents a complex setting with significant challenges in exploration, exploitation, etc. Multiple hazards exist, including environmental and structural, and conditions degrade and change temporally. We present a survey of research in autonomy, networking and mobility focused on exploring and/or mapping subsurface networks in unpredictable and unexplored environments. The focus is on mining tunnels as a proxy subterranean environment; motivated by the proximity of the authors to the Bonita Peak Mining District; a Superfund site consisting of 48 historic mines which have contaminated soil, groundwater and surface water with heavy metals as a result of historic practices. The exploration and assessment of the mining tunnels for remediation efforts presents an extremely challenging problem in subterranean navigation. Arguably, the environment in question is the most extreme and challenging subterranean environment. Unmanned assets must enter to explore and re‐map the tunnels to assess safety for subsequent entrance by robots and/or humans for proper remediation. The survey is split into three categories — Locomotion, GPS‐denied navigation and localization, and Communication. It is concluded with a proposed design for a platform that addresses the difficulties of exploring an abandoned mine

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“…Furthermore, some techniques carried out specific-object detection in conjunction with continuous-tracking strategies, such as Kalman or particle filters [19,27,28]. In References [27,28], a strategy for human following was designed and tested in real flights. In Reference [27], a miniature robotic blimp was used as the robotic platform, with human-pose estimation and standard PID control.…”

Section: Related Workmentioning

confidence: 99%

“…In References [27,28], a strategy for human following was designed and tested in real flights. In Reference [27], a miniature robotic blimp was used as the robotic platform, with human-pose estimation and standard PID control. In Reference [28], human tracking was carried out over a long period of time and long distances by means of active infrared markers and the estimation of the pose of both human and UAVs without calibrated or stationary cameras.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Multirotor Following Using Synthetic Learning Techniques

Rodríguez-Ramos

Alvarez-Fernandez

Bavle

et al. 2019

Sensors

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Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep- and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strategy for object following is introduced where the camera gimbal movement is coupled with the multirotor motion during the multirotor following. Results confirm that our present framework can be used to deploy a vision-based task in real flight using synthetic data. It was extensively validated in both simulated and real-flight scenarios, providing proper results (following a multirotor up to 1.3 m/s in simulation and 0.3 m/s in real flights).

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Monocular vision-based human following on miniature robotic blimp

Cited by 54 publications

References 16 publications

A Low-Cost, Open-Source, Robotic Airship for Education and Research

A Low-Cost, Open-Source, Robotic Airship for Education and Research

Survey of unmanned subterranean exploration, navigation, and localisation

Vision-Based Multirotor Following Using Synthetic Learning Techniques

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