Autonomous indoor object tracking with the Parrot AR.Drone

Chakrabarty, Anjan; Morris, Robert; Bouyssounouse, Xavier; Hunt, Rusty

doi:10.1109/icuas.2016.7502612

Cited by 28 publications

(17 citation statements)

References 12 publications

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“…Second, the 3 DOFs of the moving platform simplify the control strategy and save time and space in tracking the target position. In this study, this aspect was implemented with Mecanum wheels [ 23 ], but other biomimetic techniques [ 21 , 26 ] or unmanned aerial vehicles [ 22 , 27 ] can also be used.…”

Section: Discussionmentioning

confidence: 99%

User Local Coordinate-Based Accompanying Robot for Human Natural Movement of Daily Life

Chen

et al. 2021

Sensors

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Considering the trend of aging societies, accompanying technology can help frail, elderly individuals participate in daily activities. The ideal accompanying robot should accompany the user in a proper position according to the activity scenarios and context; the prerequisite is that the accompanying robot should quickly move to a designated position and closely maintain it regardless of the direction in which the user moves. This paper proposes a user local coordinate-based strategy to satisfy this need. As a proof of concept, a novel “string-pot” approach was utilized to measure the position difference between the robot and the target. We implemented the control strategy and assessed its performance in our gait lab. The results showed that the robot can follow the user in the designated position while the user performs forward, backward, and lateral movements, turning, and walking along a curve.

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Section: Discussionmentioning

confidence: 99%

User Local Coordinate-Based Accompanying Robot for Human Natural Movement of Daily Life

Chen

et al. 2021

Sensors

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“…Note that there is an increasing number of works proposing the use of UAVs in a semi-autonomous fashion to reduce operator's dependence, e.g., Yang et al (2015), and the use of this kind of system to perform surveillance, such as in Gotzy et al (2016) and Chakrabarty et al (2016). As the main contribution of this research work, human activity is recognized and monitored by a UAV through perceptual data.…”

Section: The Work Ofmentioning

confidence: 98%

Surveillance Architecture for Human Activity Recognition using Unmanned Aerial Vehicle

Pinto

Melo

Marcato

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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There is an intensive growth in researches regarding surveillance and threat detection. Surveillance tasks often involve several actors with multiple interactions. Thus, modeling a complex activity becomes challenging. This work proposes an architecture comprised of low, middle, and high levels. The low-level recognizes characteristics, positioning of objects, and time of occurrences utilizing a camera and Unmanned Aerial Vehicle (UAV) sensors. The middle-level is responsible for structuring the information from the low-level using Deterministic Finite Automata (DFA). An expert system attached in the high-level module performs inference over the organized information to enables the system to have simple reasoning modules, assisting the operator decision. The architecture is embedded in a UAV to reduce the number of cameras and to reach difficult areas. The experiments showed that the proposed system updated the grammatical structure effectively given a sequence of information computed by the vision modules.

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“…As with the ground and underwater scenarios, a single UAV follows a single person in most commercial and personal applications. Owing to the increasing popularity of these applications, research studies have also concentrated largely on this interaction paradigm (Chakrabarty et al, 2016; Lugo and Zell, 2014; Pestana et al, 2014). However, a single UAV often cannot fulfill certain application requirements, such as capturing an event from different viewpoints or over a long period of time.…”

Section: Categorization Of Autonomous Person-following Behaviorsmentioning

confidence: 99%

Person-following by autonomous robots: A categorical overview

Islam

Hong

Sattar

2019

The International Journal of Robotics Research

109

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A wide range of human-robot collaborative applications in diverse domains such as manufacturing, health care, the entertainment industry, and social interactions, require an autonomous robot to follow its human companion. Different working environments and applications pose diverse challenges by adding constraints on the choice of sensors, the degree of autonomy, and dynamics of a person-following robot. Researchers have addressed these challenges in many ways and contributed to the development of a large body of literature. This paper provides a comprehensive overview of the literature by categorizing different aspects of person-following by autonomous robots. Also, the corresponding operational challenges are identified based on various design choices for ground, underwater, and aerial scenarios. In addition, state-of-the-art methods for perception, planning, control, and interaction are elaborately discussed and their applicability in varied operational scenarios are presented. Then, some of the prominent methods are qualitatively compared, corresponding practicalities are illustrated, and their feasibility is analyzed for various usecases. Furthermore, several prospective application areas are identified, and open problems are highlighted for future research.

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Autonomous indoor object tracking with the Parrot AR.Drone

Cited by 28 publications

References 12 publications

User Local Coordinate-Based Accompanying Robot for Human Natural Movement of Daily Life

User Local Coordinate-Based Accompanying Robot for Human Natural Movement of Daily Life

Surveillance Architecture for Human Activity Recognition using Unmanned Aerial Vehicle

Person-following by autonomous robots: A categorical overview

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