Analysis of Cooperative Industrial Task Execution by Mobile and Manipulator Robots

The emergence of mobile robots as a flexible upgrade of industrial AGVs and the simultaneous diffusion of collaborative manipulators pose new problems for the organization of work in industrial plants. The new robots address work environments characterized by limited automation and unstructured layouts. Present study is aimed at demonstrating that, using commercially available technologies, it is possible to assure a fruitful collaborative interaction among three main actors of the factory of tomorrow: the human operator, the mobile robot and the manipulator.

[1]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[2]  Giulia Bruno,et al.  Dynamic task classification and assignment for the management of human-robot collaborative teams in workcells , 2018, The International Journal of Advanced Manufacturing Technology.

[3]  Sotiris Makris,et al.  RFID driven robotic assembly for random mix manufacturing , 2012 .

[4]  Ali Borji,et al.  Salient Object Detection: A Benchmark , 2012, ECCV.

[5]  Danica Kragic,et al.  Task-Based Robot Grasp Planning Using Probabilistic Inference , 2015, IEEE Transactions on Robotics.

[6]  Jörg Krüger,et al.  Safe human-robot-collaboration-introduction and experiment using ISO/TS 15066 , 2017, 2017 3rd International Conference on Control, Automation and Robotics (ICCAR).

[7]  Kalyan Ghosh,et al.  A simplified and efficient representation for evaluation and selection of assembly sequences , 2003, Comput. Ind..

[8]  Stephen Gould,et al.  Region-based Segmentation and Object Detection , 2009, NIPS.

[9]  Jianguo Tao,et al.  Optimal Energy Consumption for Mobile Manipulators Executing Door-Opening Task , 2018 .

[10]  Paolo Chiabert,et al.  Reflective workpiece detection and localization for flexible robotic cells , 2017 .

[11]  Philipp Klimant,et al.  Virtual Prototyping Technologies Enabling Resource-Efficient and Human-Centered Product Development , 2018 .

[12]  Roger V. Bostelman,et al.  Mobile manipulator stability measurements , 2017 .

[13]  Lihui Wang,et al.  Review: Advances in 3D data acquisition and processing for industrial applications , 2010 .

[14]  Francesco Leali,et al.  Survey on Human-Robot Interaction for Robot Programming in Industrial Applications , 2018 .

[15]  Zoran Miljkovic,et al.  A review of automated feature recognition with rule-based pattern recognition , 2008, Comput. Ind..

[16]  Manfred Tscheligi,et al.  Augmented reality for industrial robot programmers: Workload analysis for task-based, augmented reality-supported robot control , 2016, 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[17]  Heping Chen,et al.  Topological Indoor Localization and Navigation for Autonomous Mobile Robot , 2015, IEEE Transactions on Automation Science and Engineering.

[18]  José María Martínez Sanchez,et al.  Comparative Evaluation of Stationary Foreground Object Detection Algorithms Based on Background Subtraction Techniques , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[19]  Sonia Chernova,et al.  Interactive Hierarchical Task Learning from a Single Demonstration , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).