skip to main content
research-article

Antlab: A Multi-Robot Task Server

Published:27 September 2017Publication History
Skip Abstract Section

Abstract

We present Antlab, an end-to-end system that takes streams of user task requests and executes them using collections of robots. In Antlab, each request is specified declaratively in linear temporal logic extended with quantifiers over robots. The user does not program robots individually, nor know how many robots are available at any time or the precise state of the robots. The Antlab runtime system manages the set of robots, schedules robots to perform tasks, automatically synthesizes robot motion plans from the task specification, and manages the co-ordinated execution of the plan.

We provide a constraint-based formulation for simultaneous task assignment and plan generation for multiple robots working together to satisfy a task specification. In order to scalably handle multiple concurrent tasks, we take a separation of concerns view to plan generation. First, we solve each planning problem in isolation, with an “ideal world” hypothesis that says there are no unspecified dynamic obstacles or adversarial environment actions. Second, to deal with imprecisions of the real world, we implement the plans in receding horizon fashion on top of a standard robot navigation stack. The motion planner dynamically detects environment actions or dynamic obstacles from the environment or from other robots and locally corrects the ideal planned path. It triggers a re-planning step dynamically if the current path deviates from the planned path or if planner assumptions are violated.

We have implemented Antlab as a C++ and Python library on top of robots running on ROS, using SMT-based and AI planning-based implementations for task and path planning. We evaluated Antlab both in simulation as well as on a set of TurtleBot robots. We demonstrate that it can provide a scalable and robust infrastructure for declarative multi-robot programming.

References

  1. R. Alur, S. Moarref, and U. Topcu. 2016. Compositional synthesis of reactive controllers for multi-agent systems. In CAV.Google ScholarGoogle Scholar
  2. A. Biere, K. Heljanko, T. Junttila, T. latvala, and V. Schuppan. 2006. Linear encoding of bounded LTL model checking. LMCS, 2(5:5):1--64.Google ScholarGoogle Scholar
  3. R. Bogue. 2016. Growth in e-commerce boosts innovation in the warehouse robot market. Industrial Robot: An International Journal, 43(6):583--587.Google ScholarGoogle ScholarCross RefCross Ref
  4. H. Choset, K. M. Lynch, S. Hutchinson, G. A. Kantor, W. Burgard, L. E. Kavraki, and S. Thrun. 2005. Principles of Robot Motion. A Bradford Book.Google ScholarGoogle Scholar
  5. D. Claes. collvoid package for ROS. https://github.com/daenny/collvoid.Google ScholarGoogle Scholar
  6. community project. ROS2. https://github.com/ros2/ros2/wiki. Accessed: November 2016.Google ScholarGoogle Scholar
  7. S. S. Craciunas, A. Haas, C. M. Kirsch, H. Payer, H. Röck, A. Rottmann, A. Sokolova, R. Trummer, J. Love, and R. Sengupta. 2010. Information-acquisition-as-a-service for cyber-physical cloud computing. In HotCloud. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. A. DeCastro, J. Alonso-Mora, V. Raman, D. Rus, and H. Kress-Gazit. 2015. Collision-free reactive mission and motion planning for multi-robot systems. In ISRR.Google ScholarGoogle Scholar
  9. A. Desai, I. Saha, J. Yang, S. Qadeer, and S. A. Seshia. 2017. Drona: A framework for safe distributed mobile robotics. In ICCPS, pages 239--248. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. R. Ehlers and B. Finkbeiner. 2011. Reactive safety. In GandALF 2011, EPTCS 54, pages 178--191.Google ScholarGoogle Scholar
  11. A. Elfes. 1989. Using occupancy grids for mobile robot perception and navigation. IEEE Computer, 22(6):46--57. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. E. M. Eppstein. ROS navigation stack. http://wiki.ros.org/navigation. Indigo version.Google ScholarGoogle Scholar
  13. M. A. Erdmann and T. Lozano-Pérez. 1986. On multiple moving objects. In ICRA.Google ScholarGoogle Scholar
  14. R. Vaughan et al. Stage simulator. http://wiki.ros.org/stage. Indigo version.Google ScholarGoogle Scholar
  15. P. Eyerich, R. Mattmüller, and G. Röger. 2012. Using the context-enhanced additive heuristic for temporal and numeric planning. In Towards Service Robots for Everyday Environments, pages 49--64. Springer.Google ScholarGoogle Scholar
  16. R. Fikes and N. J. Nilsson. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artif. Intell., 2(3/4):189--208.Google ScholarGoogle ScholarCross RefCross Ref
  17. E. Filiot, N. Jin, and J.-F. Raskin. 2011. Antichains and compositional algorithms for LTL synthesis. FMSD, 39(3):261--296. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. C. Finucane, Gangyuan Jing, and H. Kress-Gazit. 2010. LTLMoP: experimenting with language, temporal logic and robot control. In IROS, pages 1988--1993.Google ScholarGoogle Scholar
  19. B. Gerkey and V. Rabaud. Slam gmapping package. https://github.com/ros-perception/slam_gmapping. Indigo version.Google ScholarGoogle Scholar
  20. M. Ghallab, C. Aeronautiques, C. K. Isi, and D. Wilkins. 1998. PDDL: The planning domain definition language. Technical Report CVC TR98003/DCS TR1165, Yale Center for Computational Vision and Control.Google ScholarGoogle Scholar
  21. E. Guizzo. 2008. Three engineers, hundreds of robots, one warehouse. IEEE Spectrum, 45(7):26--34. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Y. Guo and L.E. Parker. 2002. A distributed and optimal motion planning approach for multiple mobile robots. In ICRA.Google ScholarGoogle Scholar
  23. D. Hennes, D. Claes, W. Meeussen, and K. Tuyls. 2012. Multi-robot collision avoidance with localization uncertainty. In AAMAS, pages 147--154. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jörg Hoffmann. 2003. The metric-ff planning system: Translating ”ignoring delete lists” to numeric state variables. J. Artif. Int. Res., 20(1):291--341. Google ScholarGoogle ScholarCross RefCross Ref
  25. W. N. N. Hung, X. Song, J. Tan, X. Li, J. Zhang, R. Wang, and P. Gao. 2014. Motion planning with Satisfiability Modulo Theroes. In ICRA, pages 113--118.Google ScholarGoogle Scholar
  26. H. Kress-Gazit, G. E. Fainekos, and G. J. Pappas. 2009. Temporal-logic-based reactive mission and motion planning. IEEE Transactions on Robotics. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. H. J. Levesque, R. Reiter, Y. Lespérance, F. Lin, and R. B. Scherl. 1997. GOLOG: A logic programming language for dynamic domains. J. Log. Program., 31(1-3):59--83.Google ScholarGoogle ScholarCross RefCross Ref
  28. V. Lifschitz and W. Ren. 2006. A modular action description language. In AAAI, pages 853--859. AAAI Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Y. Lin and S. Mitra. 2015. StarL: Towards a unified framework for programming, simulating and verifying distributed robotic systems. In LCTES. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. L. De Moura and N. BjÃÿrner. 2008. Z3: an efficient smt solver. In TACAS, pages 337--340. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. S. Nedunuri, S. Prabhu, M. Moll, S. Chaudhuri, and L. E. Kavraki. 2014. SMT-based synthesis of integrated task and motion plans from plan outlines. In ICRA.Google ScholarGoogle Scholar
  32. M. Quigley, K. Conley, B. P. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng. 2009. ROS: an open-source Robot Operating System. In ICRA Workshop on Open Source Software.Google ScholarGoogle Scholar
  33. V. Raman, N. Piterman, and H. Kress-Gazit. 2013. Provably correct continuous control for high-level robot behaviors with actions of arbitrary execution durations. In ICRA, pages 4075--4081.Google ScholarGoogle Scholar
  34. S. Russell and P. Norvig. 2009. Artificial Intelligence: A Modern Approach. Pearson. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. I. Saha, R. Ramaithitima, V. Kumar, G. J. Pappas, and S. A. Seshia. 2014. Automated composition of motion primitives for multi-robot systems from safe ltl specifications. In IROS, pages 1525--1532. IEEE.Google ScholarGoogle Scholar
  36. I. Saha, R. Ramaithitima, V. Kumar, G. J. Pappas, and S. A. Seshia. 2016. Implan: Scalable incremental motion planning for multi-robot systems. In ICCPS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. M. Saska, V. Vonásek, J. Chudoba, J. Thomas, G. Loianno, and V. Kumar. 2016. Swarm distribution and deployment for cooperative surveillance by micro-aerial vehicles. J. Intelligent 8 Robotic Systems, pages 1--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. M. Turpin, K. Mohta, N. Michael, and V. Kumar. 2013. Goal assignment and trajectory planning for large teams of aerial robots. In RSS.Google ScholarGoogle Scholar
  39. J. van den Berg and M. Overmars. 2005. Prioritized motion planning for multiple robots. In IROS.Google ScholarGoogle Scholar
  40. M. Čáp, P. Novák, M. Selecký, J. Faigl, and J. Vokřínek. 2013. Asynchronous decentralized prioritized planning for coordination in multi-robot system. In IROS.Google ScholarGoogle Scholar
  41. P. Velagapudi, K. Sycara, and P. Scerri. 2010. Decentralized prioritized planning in large multirobot teams. In IROS.Google ScholarGoogle Scholar
  42. Y. Wang, N. T. Dantam, S. Chaudhuri, and L. E. Kavraki. 2016. Task and motion policy synthesis as liveness games. In ICAPS, page 536. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. T. Wongpiromsarn, U. Topcu, and R. M. Murray. 2012. Receding horizon temporal logic planning. IEEE Trans. Automat. Contr.Google ScholarGoogle ScholarCross RefCross Ref
  44. M. Wulfraat. Is Kiva systems a good fit for your distribution center? an unbiased distribution consultant evaluation. http://www.mwpvl.com/html/kiva_systems.html. Accessed: October 2016.Google ScholarGoogle Scholar

Index Terms

  1. Antlab: A Multi-Robot Task Server

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!