Hammer: An Android based application for end-user industrial robot programming

Mateo, Carlos M.; Brunete, Alberto; Gambao, Ernesto; Hernando, Miguel

doi:10.1109/mesa.2014.6935597

Cited by 49 publications

(35 citation statements)

References 9 publications

(10 reference statements)

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“…Abbas et al [7] proposed the usage of mobile devices in combination with AR for industrial robot online programming. Mateo et al [8] state that the use of Android tablets in industrial robot working environments is growing and provides new means for small batch industrial applications that need fast and easy to use tools to program the robots. Tang et al [9] conducted a user study regarding the relative effectiveness of AR instructions in an assembly task.…”

Section: Introductionmentioning

confidence: 99%

Augmented reality for industrial robot programmers: Workload analysis for task-based, augmented reality-supported robot control

Stadler

Kain

Giuliani

et al. 2016

2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Section: Introductionmentioning

confidence: 99%

Augmented reality for industrial robot programmers: Workload analysis for task-based, augmented reality-supported robot control

Stadler

Kain

Giuliani

et al. 2016

2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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“…On the AR side of things, projector based AR has seen use for visualizing robot's intentions and intuitive programming of robots in robot work cells [37], displaying intentions of mobile robots operating in human environments [38], as well as for debugging and rapid prototyping of robotic systems through visualization of sensor data [39]. Tablets have seen use in AR-assisted robot programming [40] as well. Devices like the Google Tango tablet, in addition, with inbuilt SLAM [41] can be used for markerless AR applications.…”

Section: Related Workmentioning

confidence: 99%

Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

Petković

Puljiz

Marković

et al. 2019

Robotics and Computer-Integrated Manufacturing

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With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results.

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“…These developments have led to a growing interest in making it easy for domain experts to transfer knowledge to collaborative robots, either through a user interface [2], [3], [4], [5], natural language [6], or learning from demonstration [7], [8], [9], [10]. To take full advantage of these systems, the human user must have an accurate mental model of a robot's capabilities [11].…”

Section: Introductionmentioning

confidence: 99%

“…We previously proposed the CoSTAR system as a potential solution. 3 CoSTAR offers a set of planning and perceptual capabilities, united through a Behavior Tree-based task plan editor, that is designed to enable expert users to author complex plans for collaborative robots. We also demonstrated that CoSTAR allows users to solve a wide variety of problems such as sanding, assembly, and wire bending [5].…”

Section: Introductionmentioning

confidence: 99%

“…Recent approaches for end-user instruction of collaborative robots include the development of new user interfaces [2], [3], [4], learning from demonstration [7], [10], or systems that make use of natural language together with ontologies and large knowledge bases to follow high-level instructions, such as Tell Me Dave [6] or RoboSherlock [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Methods for End-User Creation of Robot Task Plans

Paxton

Jonathan

Hundt

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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How can we enable users to create effective, perception-driven task plans for collaborative robots? We conducted a 35-person user study with the Behavior Tree-based CoSTAR system to determine which strategies for end user creation of generalizable robot task plans are most usable and effective. CoSTAR allows domain experts to author complex, perceptually grounded task plans for collaborative robots. As a part of CoSTAR's wide range of capabilities, it allows users to specify SmartMoves: abstract goals such as "pick up component A from the right side of the table." Users were asked to perform pick-and-place assembly tasks with either SmartMoves or one of three simpler baseline versions of CoSTAR. Overall, participants found CoSTAR to be highly usable, with an average System Usability Scale score of 73.4 out of 100. SmartMove also helped users perform tasks faster and more effectively; all SmartMove users completed the first two tasks, while not all users completed the tasks using the other strategies. SmartMove users showed better performance for incorporating perception across all three tasks.

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Hammer: An Android based application for end-user industrial robot programming

Cited by 49 publications

References 9 publications

Augmented reality for industrial robot programmers: Workload analysis for task-based, augmented reality-supported robot control

Augmented reality for industrial robot programmers: Workload analysis for task-based, augmented reality-supported robot control

Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

Evaluating Methods for End-User Creation of Robot Task Plans

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