Intuitive robot tasks with augmented reality and virtual obstacles

Gaschler, Andre; Springer, Maximilian; Rickert, Markus; Knoll, Alois

doi:10.1109/icra.2014.6907747

Cited by 39 publications

(19 citation statements)

References 18 publications

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“…The fact that SMEs extend the requirement profile for industrial robots, Schraft and Meyer [1], is of particular interest for us. SMEs typically have small lot size, short production cyles, unstructured environments and do not employ experts with robotic knowledge [2]. We use augmented reality (AR) as an approach to enhance industrial robot programming.Industrial robot programming and maintainance is not done on a daily basis.…”

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.…”

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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“…In [4], [8], the authors presented an approach for reuseable hardware-independent skills for robots. Rather than using CAD semantics, task-based programming approaches may use 3D pointing devices or other novel interfaces to program robots intuitively [9], [10], [6].…”

Section: Related Workmentioning

confidence: 99%

Constraint-based task programming with CAD semantics: From intuitive specification to real-time control

Somani

Gaschler

Rickert

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we propose a framework for intuitive task-based programming of robots using geometric interrelational constraints. The intended applications of this framework are robot programming interfaces that use semantically rich task descriptions, allow intuitive (re-)programming, and are suitable for non-expert users typically found in SMEs. A key concept in this work is the use of CAD semantics to represent geometric entities in the robotic workcell. The robot tasks are then represented as a set of geometrical inter-relational constraints, which are solved in real-time to be executed on the robot. Since these constraints often specify the target pose only partially, the robot can be controlled to move in the constraints' null space in order to handle external disturbances or further optimize the robot's pose during runtime. Geometrical interrelational constraints are easy to understand and can be intuitively specified using CAD software. A number of applications common in industrial robotic scenarios have been chosen to highlight the advantages of the presented approach vis-à-vis the state-of-the-art approaches.

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Intuitive robot tasks with augmented reality and virtual obstacles

Cited by 39 publications

References 18 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

Constraint-based task programming with CAD semantics: From intuitive specification to real-time control

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