Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-12T19:47:13.623Z Has data issue: false hasContentIssue false
  • English
  • Français

Integrated real-time task and motion planning for multiple robots under path and communication uncertainties

Published online by Cambridge University Press:  16 November 2017

Bradley Woosley Open the ORCID record for Bradley Woosley [Opens in a new window]  and
Prithviraj Dasgupta
Bradley Woosley*
Affiliation:
Computer Science Department, University of Nebraska Omaha, Omaha, Nebraska, USA E-mail: [email protected]
Prithviraj Dasgupta
Affiliation:
Computer Science Department, University of Nebraska Omaha, Omaha, Nebraska, USA E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

We consider a problem where robots are given a set of task locations to visit with coarsely known distances. The robots must find the task ordering that reduces the overall distance to visit the tasks. We propose an abstraction that models the uncertainty in the paths, and a Markov Decision Process-based algorithm that selects paths that reduces the expected distance to visit the tasks. We also describe a distributed coordination algorithm to resolve path conflicts. We have shown that our task selection is optimal, our coordination is deadlock-free, and have experimentally verified our approach in hardware and simulation.

Keywords

Multi-robotSimultaneous task and motion planningExplorationPath uncertaintyReal time
Type
Articles
Information
Robotica , Volume 36 , Issue 3 , March 2018 , pp. 353 - 373
Copyright
Copyright © Cambridge University Press 2017 

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

1. Choset, H., Burgard, W., Hutchinson, S., Kantor, G., Kavraki, L., Kynch, K. and Thrun, S., Principles of Robot Motion: Theory, Algorithms, and Implementation (MIT Press, 2005) Cambridge, MA.Google Scholar
2. Claes, D., Robbel, P., Olihoek, F. A., Tuyls, K., Hennes, D. and Hoek, W., “Effective Approximations for Multi-Robot Coordination in Spatially Distributed Tasks,” Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Istanbul, Turkey (2015) pp 881–890.Google Scholar
3. Cormen, T., Leiserson, C., Rivest, R. and Stein, C., Introduction to Algorithms (MIT Press, 2009) Cambridge, MA.Google Scholar
4. Desaraju, T. and How, J., “Decentralized path planning for multi-agent teams with complex constraints,” Auton. Robots 32 (4), 385–403 (2012).Google Scholar
5. Ferreira, P. R., Santos, F. and Bazzan, A. L., “RoboCup Rescue as Multiagent Task Allocation among Teams: Experiments with Task Interdependencies,” Autonomous Agents and Multi-Agent Systems, Budapest (2009) pp. 421–443.Google Scholar
6. Gammell, J., Srinivasa, S. and Barfoot, T., “Informed RRT*: Optimal Sampling-Based Path Planning Focused Via Direct Sampling of an Admissible Ellipsoidal Heuristic,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, Illinois (2014) pp. 2997–3004.Google Scholar
7. Garcia-Molina, H., “Elections in a distributed computing system,” IEEE Trans. Comput. 31 (1), 48–59 (1982).CrossRefGoogle Scholar
8. Gerkey, B. and Mataric, M., “A formal analysis and taxonomy of task allocation in multi-robot systems,” Int. J. Robot. Res. 23 (9), 939–954 (2004).CrossRefGoogle Scholar
9. Hauser, K., “Lazy Collision Checking in Asymptotically-Optimal Motion Planning,” Proceedings of the IEEE International Conference on Robotics and Automation, Seattle, Washington (2015) pp 2951–2957.CrossRefGoogle Scholar
10. Kaelbling, L. and Lozano-Pérez, T., “Integrated task and motion planning in belief space,” Int. J. Robot. Res. 32 (9–10), 1194–1227.Google Scholar
11. Khamis, A., Hussein, A., Elmogy, A., Koubaa, A. and Dios, J. R. M, “Multi-robot task Allocation: A review of the state-of-the-art,” Coop. Robots Sensor Netw. 604, 31–51 (2015).Google Scholar
12. Korsah, G. A., Stentz, A. and Dias, M. B., “A comprehensive taxonomy for mult-robot task allocation,” Int. J. Robot. Res. 32 (11), 1495–1512 (2013).Google Scholar
13. Lenagh, W., Dasgupta, P. and Muñoz-Meléndez, A., “A spatial queuing-based algorithm for multi-robot task allocation,” Robotics 4 (3), 316–340 (2005).CrossRefGoogle Scholar
14. Liu, L. and Shell, D. A., “An anytime assignment algorithm: From local task swapping to global optimality,” Auton. Robots 35 (4), 271–286 (2013).Google Scholar
15. Liu, L. and Shell, D. A., “Large-scale multi-robot task allocation via dynamic partitioning and distribution,” Auton. Robots 33 (3), 291–307 (2012).CrossRefGoogle Scholar
16. Liu, L. and Michael, N., “An MDP-Based Approximation Method for Goal Constrained Multi-MAV Planning Under Uncertainty,” Proceedings of the IEEE International Conference on Robotics and Automation, Stockholm, Sweden (2016) pp. 56–62.Google Scholar
17. Liu, L., Michael, N. and Shell, D. A., “Communication constrained task allocation with optimized local task swaps,” Auton. Robots 39 (3), 429–444 (2015).Google Scholar
18. Loibl, S., Meyer-Delius, D. and Pfaff, P., “Probabilistic Time-Dependent Models for Mobile Robot Path Planning in Changing Environments,” Proceedings of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany (2013) pp. 5545–5550.Google Scholar
19. Luna, R. and Bekris, K., “Efficient and Complete Centralized Multi-Robot Path Planning,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, California (2011) pp. 3268–3275.Google Scholar
20. Missiuro, P. and Roy, N., “Adapting Probabilistic Roadmaps to Handle Uncertain Maps,” Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, Florida (2006) pp. 1261–1267.Google Scholar
21. Nanjanath, M. and Gini, M. L., “Repeated auctions for robust task execution by a robot team,” Robot. Autom. Syst. 58 (7), 900–909 (2010).CrossRefGoogle Scholar
22. Russel, S. and Norvig, P., Artificial Intelligence: A Modern Approach. (Prentice Hall, 2009) Upper Saddle River, NJ.Google Scholar
23. Rutishauser, S., Correll, N. and Martinoli, A., “Collaborative coverage using a swarm of networked miniature robots,” Robot. Auton. Syst. 57 (5), 517–525 (2009).CrossRefGoogle Scholar
24. Sucan, I. and Kavraki, L., “Accounting for Uncertainty in Simultaneous Task and Motion Planning Using Task Motion Multigraphs,” Proceedings of the IEEE International Conference on Robotics and Automation, St. Paul Minnesota (2012) pp. 4822–4828.Google Scholar
25. Voss, C., Moll, M. and Kavraki, L., “A Heuristic Approach to Finding Diverse Short Paths,” Proceedings of the IEEE International Conference on Robotics and Automation, Seattle, Washington (2015) pp. 4822–4828.Google Scholar
26. Wagner, G. and Choset, H., “Subdimensional expansion for multirobot path planning,” Artif. Intell. 219, 1–24 (2015).Google Scholar
27. Wolfe, J., Marthi, B. and Russel, S., “Combined Task and Motion Planning for Mobile Manipulation,” Proceedings of the International Conference on Automated Planning and Scheduling, Toronto, Canada (2010) pp. 254–257.Google Scholar
28. Woosley, B. and Dasgupta, P., “Multirobot Task Allocation with Real-Time Path Planning,” Proceedings of the International FLAIRS Conference, St. Pete Beach, Florida (2013) pp. 574–579.Google Scholar
29. Woosley, B. and Dasgupta, P., “Integrated Task and Motion Planning for Multiple Robots under Path and Communication Uncertainties,” Technical Report, arXiv:1607.00562 [cs.RO], (2016).Google Scholar
30. Zlot, R. and Stentz, A., “Market-based multirobot coordination for complex tasks,” Int. J. Robot. Res. 25 (1), 73–101 (2006).Google Scholar