Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-08T18:33:56.476Z Has data issue: false hasContentIssue false

Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach

Published online by Cambridge University Press:  22 September 2020

Momina Rizwan
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanc University, Istanbul, [email protected], [email protected], [email protected]
Volkan Patoglu
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanc University, Istanbul, [email protected], [email protected], [email protected]
Esra Erdem
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanc University, Istanbul, [email protected], [email protected], [email protected]

Abstract

For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans’ behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture.

Type
Original Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baral, C., Kreinovich, V., and Trejo, R. 1999. Computational complexity of planning and approximate planning in presence of incompleteness. In International Joint Conference on Artificial Intelligence. 948–955.Google Scholar
Brewka, G., Eiter, T., and Truszczynski, M. 2016. Answer set programming: An introduction to the special issue. AI Magazine 37, 3, 56.Google Scholar
Eiter, T., Ianni, G., Schindlauer, R., and Tompits, H. 2005. A Uniform Integration of Higher-Order Reasoning and External Evaluations in Answer-Set Programming. In International Joint Conference on Artificial Intelligence. 90–96.Google Scholar
Giuliani, M., Petrick, R., Foster, M. E., Gaschler, A., Isard, A., Pateraki, M., and Sigalas, M. 2013. Comparing task-based and socially intelligent behaviour in a robot bartender. In ACM on International Conference on Multimodal Interaction. 263–270.Google Scholar
Grigore, E. C. and Scassellati, B. 2016. Constructing policies for supportive behaviors and communicative actions in human-robot teaming. In ACM/IEEE International Conference on Human-Robot Interaction. 615–616.Google Scholar
Karaman, S. and Frazzoli, E. 2011. Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research 30, 7, 846894.CrossRefGoogle Scholar
Kim, J., Banks, C. J., and Shah, J. A. 2017. Collaborative planning with encoding of users’ high-level strategies. In AAAI. 955–962.Google Scholar
Lasota, P. A. and Shah, J. A. 2015. Analyzing the effects of human-aware motion planning on close-proximity human–robot collaboration. Human factors 57, 1, 2133.Google Scholar
Peot, M. A. and Smith, D. E. 1992. Conditional nonlinear planning. In Artificial Intelligence Planning Systems. Elsevier, 189–197.Google Scholar
Petrick, R. P. and Foster, M. E. 2013. Planning for social interaction in a robot bartender domain. In International Conference on Automated Planning and Scheduling.Google Scholar
Pryor, L. and Collins, G. 1996. Planning for contingencies: A decision-based approach. Journal of Artificial Intelligence Research 4, 287339.Google Scholar
Sebastiani, E., Lallement, R., Alami, R., and Iocchi, L. 2017. Dealing with on-line human-robot negotiations in hierarchical agent-based task planner. In International Conference on Automated Planning and Scheduling.Google Scholar
Şucan, I. A., Moll, M., and Kavraki, L. E. 2012. The Open Motion Planning Library. IEEE Robotics and Automation Magazine 19, 4, 7282.CrossRefGoogle Scholar
Tellex, S., Knepper, R. A., Li, A., Rus, D., and Roy, N. 2014. Asking for help using inverse semantics. In Robotics Science and Systems.Google Scholar
Unhelkar, V. V., Siu, H. C., and Shah, J. A. 2014. Comparative performance of human and mobile robotic assistants in collaborative fetch-and-deliver tasks. In ACM/IEEE International Conference on Human-Robot Interaction. 82–89.Google Scholar
Warren, D. H. D. 1976. Generating conditional plans and programs. In Summer Conference on Artificial Intelligence and Simulation of Behaviour. 344–354.Google Scholar
Yalciner, I. F., Nouman, A., Patoglu, V., and Erdem, E. 2017. Hybrid conditional planning using answer set programming. Theory and Practice of Logic Programming 17, 5-6, 10271047.CrossRefGoogle Scholar