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Robot action planning by online optimization in human–robot collaborative tasks

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Abstract

Collaborative robots are widely used in strict and complex hybrid assembly tasks in the context of intelligent manufacturing. However, the task efficiency and task cost evaluation, which are very significant in the robot action planning to ensure and enhance the task quality in the human–robot collaboration process, are rarely studied in previous works. In this paper, we propose a novel and practical approach based on online optimization for the robot to plan its actions in human–robot collaboration to address this challenge. First, we extract the task model by graphical representations and design the collaboration cost functions which incorporate time consumption and human efforts. After that, the robot action planning algorithms are developed to online plan robot actions by optimizing the collaborative assembly cost. In addition, appropriate robot actions can be planned by the proposed algorithms to ensure the accomplishment of assembly process when the human happens to conduct wrong actions during the collaboration. We performed studies through hybrid assembly tasks with different types of human–robot collaboration scenarios to verify the advantages of our robot action planning algorithms. Experimental results suggested that the proposed algorithms can successfully generate the optimal actions for the robot to guarantee the efficiency in human–robot collaborative assembly.

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References

  • Krüger, J., Bernhardt, R., Surdilovic, D., Spur, G.: Intelligent assist systems for flexible assembly. CIRP Ann. Manuf. Technol. 55, 29–32 (2006)

    Article  Google Scholar 

  • Helms, E., Schraft, R.D., Hagele, M.: rob@ work: Robot assistant in industrial environments. In: 11th IEEE International Workshop on Robot and Human Interactive Communication, 2002. Proceedings, pp. 399–404 (2002)

  • Lauria, S., Bugmann, G., Kyriacou, T., Klein, E.: Mobile robot programming using natural language. Robot. Auton. Syst. 38, 171–181 (2002)

    Article  Google Scholar 

  • Jia, Y., She, L., Cheng, Y., Bao, J., Chai, J.Y., Xi, N.: Program robots manufacturing tasks by natural language instructions. In: 2016 IEEE International Conference on Automation Science and Engineering (CASE), pp. 633–638 (2016)

  • Koppula, H.S., Gupta, R., Saxena, A.: Learning human activities and object affordances from rgb-d videos. Int. J. Robot. Res. 32, 951–970 (2013)

    Article  Google Scholar 

  • Ferreira, M., Costa, P., Rocha, L., Moreira, A.P.: Stereo-based real-time 6-DoF work tool tracking for robot programing by demonstration. Int. J. Adv. Manuf. Technol. 85, 1–13 (2014)

    Google Scholar 

  • Javaid, M., Žefran, M., Yavolovsky, A.: Using pressure sensors to identify manipulation actions during human physical interaction. In: 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2015, pp. 670–675 (2015)

  • Krüger, J., Lien, T.K., Verl, A.: Cooperation of human and machines in assembly lines. CIRP Ann. Manuf. Technol. 58, 628–646 (2009)

    Article  Google Scholar 

  • Bley, H., Reinhart, G., Seliger, G., Bernardi, M., Korne, T.: Appropriate human involvement in assembly and disassembly. CIRP Ann. Manuf. Technol. 53, 487–509 (2004)

    Article  Google Scholar 

  • Shah, J., Breazeal, C.: An empirical analysis of team coordination behaviors and action planning with application to human–robot teaming. Hum. Factors J. Hum. Factors Ergon. Soc. 52, 234–245 (2010)

    Article  Google Scholar 

  • Lien, T., Rasch, F.: Hybrid automatic-manual assembly systems. CIRP Ann. Manuf. Technol. 50, 21–24 (2001)

    Article  Google Scholar 

  • Hoffman, G., Breazeal, C.: Cost-based anticipatory action selection for human–robot fluency. IEEE Trans. Robot. 23, 952–961 (2007)

    Article  Google Scholar 

  • Hayes, B., Scassellati, B.: Effective robot teammate behaviors for supporting sequential manipulation tasks. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6374–6380 (2015)

  • Shah, J., Wiken, J., Williams, B., Breazeal, C.: Improved human-robot team performance using chaski, a human-inspired plan execution system. In: Proceedings of the 6th International Conference on Human-Robot Interaction, pp. 29–36 (2011)

  • Schraft, R., Helms, E., Hans, M., Thiemermann, S.: Man-machine-interaction and co-operation for mobile and assisting robots. In: Proceedings of Fourth International ICSC Symposium on Engineering of Intelligent Systems (EIS 2004), Madeira, Portugal (2004)

  • Schröter, D., Jaschewski, P., Kuhrke, B., Verl, A.: Methodology to identify applications for collaborative robots in powertrain assembly. Procedia CIRP 55, 12–172016 (2016)

    Article  Google Scholar 

  • Baraglia, J., Cakmak, M., Nagai, Y., Rao, R., Asada, M.: Initiative in robot assistance during collaborative task execution. In: 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 67–74 (2016)

  • Johannsmeier, L., Haddadin, S.: A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Robot. Autom. Lett. 2, 41–48 (2017)

    Article  Google Scholar 

  • Hayes, B., Scassellati, B.: Autonomously constructing hierarchical task networks for planning and human-robot collaboration. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 5469–5476 (2016)

  • Bodenhagen, L., Fugl, A.R., Jordt, A., Willatzen, M., Andersen, K.A., Olsen, M.M., et al.: An adaptable robot vision system performing manipulation actions with flexible objects. IEEE Trans. Autom. Sci. Eng. 11, 749–765 (2014)

    Article  Google Scholar 

  • Li, G., Zhu, C., Du, J., Cheng, Q., Sheng, W., Chen, H.: Robot semantic mapping through wearable sensor-based human activity recognition. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 5228–5233 (2012)

  • Guadarrama, S., Riano, L., Golland, D., Go, D., Jia, Y., Klein, D., et al.: Grounding spatial relations for human-robot interaction. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1640–1647 (2013)

  • Peternel, L., Babič, J.: Humanoid robot posture-control learning in real-time based on human sensorimotor learning ability. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 5329–5334 (2013)

  • Frank, B., Becker, M., Stachniss, C., Burgard, W., Teschner, M.: Learning cost functions for mobile robot navigation in environments with deformable objects. In: Workshop on Path Planning on Cost Maps at the IEEE International Conference on Robotics and Automation (ICRA) (2008)

  • Devaurs, D., Siméon, T., Cortés, J.: Optimal path planning in complex cost spaces with sampling-based algorithms. IEEE Trans. Autom. Sci. Eng. 13, 415–424 (2016)

    Article  Google Scholar 

  • Garcia, M.P., Montiel, O., Castillo, O., Sepúlveda, R., Melin, P.: Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Appl. Soft Comput. 9, 1102–1110 (2009)

    Article  Google Scholar 

  • Yin, H., Paiva, A., Billard, A.: Learning cost function and trajectory for robotic writing motion. In: 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 608–615 (2014)

  • Wulfmeier, M., Wang, D.Z., Posner, I.: Watch this: scalable cost-function learning for path planning in urban environments. arXiv preprint arXiv:1607.02329 (2016)

  • Rimon, E., Koditschek, D.E.: Exact robot navigation using cost functions: the case of distinct spherical boundaries in E n. In: 1988 IEEE International Conference on Proceedings Robotics and Automation, pp. 1791–1796 (1988)

  • Martinez-Cantin, R., de Freitas, N., Doucet, A., Castellanos, J.A.: Active policy learning for robot planning and exploration under uncertainty. In: Robotics: science and systems, pp. 321–328 (2007)

  • Neumann, G., Maass, W., Peters, J.: Learning complex motions by sequencing simpler motion templates. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 753–760 (2009)

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Authors and Affiliations

  1. Department of Automotive Engineering, Clemson University, Greenville, SC, 29607, USA

    Weitian Wang, Rui Li, Z. Max Diekel & Yunyi Jia

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  1. Weitian Wang
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  2. Rui Li
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  3. Z. Max Diekel
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  4. Yunyi Jia
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Correspondence to Yunyi Jia.

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Wang, W., Li, R., Diekel, Z.M. et al. Robot action planning by online optimization in human–robot collaborative tasks. Int J Intell Robot Appl 2, 161–179 (2018). https://doi.org/10.1007/s41315-018-0054-x

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  • DOI: https://doi.org/10.1007/s41315-018-0054-x

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