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.
- R. Alur, S. Moarref, and U. Topcu. 2016. Compositional synthesis of reactive controllers for multi-agent systems. In CAV.Google Scholar
- 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 Scholar
- R. Bogue. 2016. Growth in e-commerce boosts innovation in the warehouse robot market. Industrial Robot: An International Journal, 43(6):583--587.Google Scholar
Cross Ref
- 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 Scholar
- D. Claes. collvoid package for ROS. https://github.com/daenny/collvoid.Google Scholar
- community project. ROS2. https://github.com/ros2/ros2/wiki. Accessed: November 2016.Google Scholar
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Digital Library
- R. Ehlers and B. Finkbeiner. 2011. Reactive safety. In GandALF 2011, EPTCS 54, pages 178--191.Google Scholar
- A. Elfes. 1989. Using occupancy grids for mobile robot perception and navigation. IEEE Computer, 22(6):46--57. Google Scholar
Digital Library
- E. M. Eppstein. ROS navigation stack. http://wiki.ros.org/navigation. Indigo version.Google Scholar
- M. A. Erdmann and T. Lozano-Pérez. 1986. On multiple moving objects. In ICRA.Google Scholar
- R. Vaughan et al. Stage simulator. http://wiki.ros.org/stage. Indigo version.Google Scholar
- 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 Scholar
- 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 Scholar
Cross Ref
- E. Filiot, N. Jin, and J.-F. Raskin. 2011. Antichains and compositional algorithms for LTL synthesis. FMSD, 39(3):261--296. Google Scholar
Digital Library
- C. Finucane, Gangyuan Jing, and H. Kress-Gazit. 2010. LTLMoP: experimenting with language, temporal logic and robot control. In IROS, pages 1988--1993.Google Scholar
- B. Gerkey and V. Rabaud. Slam gmapping package. https://github.com/ros-perception/slam_gmapping. Indigo version.Google Scholar
- 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 Scholar
- E. Guizzo. 2008. Three engineers, hundreds of robots, one warehouse. IEEE Spectrum, 45(7):26--34. Google Scholar
Digital Library
- Y. Guo and L.E. Parker. 2002. A distributed and optimal motion planning approach for multiple mobile robots. In ICRA.Google Scholar
- D. Hennes, D. Claes, W. Meeussen, and K. Tuyls. 2012. Multi-robot collision avoidance with localization uncertainty. In AAMAS, pages 147--154. Google Scholar
Digital Library
- 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 Scholar
Cross Ref
- 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 Scholar
- H. Kress-Gazit, G. E. Fainekos, and G. J. Pappas. 2009. Temporal-logic-based reactive mission and motion planning. IEEE Transactions on Robotics. Google Scholar
Digital Library
- 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 Scholar
Cross Ref
- V. Lifschitz and W. Ren. 2006. A modular action description language. In AAAI, pages 853--859. AAAI Press. Google Scholar
Digital Library
- Y. Lin and S. Mitra. 2015. StarL: Towards a unified framework for programming, simulating and verifying distributed robotic systems. In LCTES. Google Scholar
Digital Library
- L. De Moura and N. BjÃÿrner. 2008. Z3: an efficient smt solver. In TACAS, pages 337--340. Google Scholar
Digital Library
- 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 Scholar
- 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 Scholar
- 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 Scholar
- S. Russell and P. Norvig. 2009. Artificial Intelligence: A Modern Approach. Pearson. Google Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- M. Turpin, K. Mohta, N. Michael, and V. Kumar. 2013. Goal assignment and trajectory planning for large teams of aerial robots. In RSS.Google Scholar
- J. van den Berg and M. Overmars. 2005. Prioritized motion planning for multiple robots. In IROS.Google Scholar
- 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 Scholar
- P. Velagapudi, K. Sycara, and P. Scerri. 2010. Decentralized prioritized planning in large multirobot teams. In IROS.Google Scholar
- 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 Scholar
Digital Library
- T. Wongpiromsarn, U. Topcu, and R. M. Murray. 2012. Receding horizon temporal logic planning. IEEE Trans. Automat. Contr.Google Scholar
Cross Ref
- 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 Scholar
Index Terms
Antlab: A Multi-Robot Task Server
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