# Dynamic teams of robots as ad hoc distributed computers: reducing the complexity of multi-robot motion planning via subspace selection

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## Abstract

We solve the multi-robot path planning problem using three complimentary techniques: (1) robots that must coordinate to avoid collisions form temporary dynamic teams. (2) Robots in each dynamic team become a distributed computer by pooling their computational resources over ad hoc wireless Ethernet. (3) The computational complexity of each team’s problem is reduced by carefully constraining the environmental subspace in which the problem is considered. An important contribution of this work is a method for quickly choosing the subspace, used for (3), to which each team’s problem is constrained. The heuristic is based on a tile-like pebble motion game, and returns *true* only if a subset of the environment will permit a solution to be found (otherwise it returns *false*). We perform experiments with teams of four and six CU Prairiedog robots (built on the iRobot Create platform) deployed in a large residence hall, as well as ten robots in random simulated environments.

## Keywords

Motion planning Multi robot team Ad hoc distributed computer Any-Com Dynamic team## References

- Al-Wahedi, K. (2000).
*A hybrid local-global motion planner for multi-agent coordination*. Masters thesis, Case Western Reserve University.Google Scholar - Allred, J., Hasan, A. B., Panichsakul, S., Pisano, W., Gray, P., Huang, J., et al. (2007). Sensorflock: An airborne wireless sensor network of micro-air vehicles. In
*Proceedings of the 5th international conference on embedded networked sensor systems*(pp. 117–129).Google Scholar - Amstutz, P., Correll, N., & Martinoli, A. (2009). Distributed boundary coverage with a team of networked miniature robots using a robust market-based algorithm.
*Annals of Mathematics and Artifcial Intelligence Special Issue on Coverage, Exploration, and Search*,*52*(2–4), 307–333.MathSciNetzbMATHGoogle Scholar - Arrichiello, F., Das, J., Heidarsson, H., Pereira, A., Chiaverini, S., & Sukhatme, G. S. (2009). Multi-robot collaboration with range-limited communication: Experiments with two underactuated ASVs. In
*International conference on field and service robots*.Google Scholar - Auletta, V., Monti, A., Parente, M., & Persiano, P. (1999). A linear-time algorithm for the feasibility of pebble motion on trees.
*Algorithmica*,*23*(3), 223–245.MathSciNetCrossRefGoogle Scholar - Best, G., Cliff, O., Patten, T., Mettu, R., & Fitch, R. (2016). Decentralised monte carlo tree search for active perception. In:
*International workshop on the algorithmic foundations of robotics (WAFR)*, San Francisco, USA.Google Scholar - Clark, C. M., Rock, S. M., & Latombe, J. C. (2003). Motion planning for multiple mobile robots using dynamic networks. In
*IEEE international conference on robotics and automation, 2003. Proceedings. ICRA’03*(Vol. 3, pp. 4222–4227). IEEE.Google Scholar - de Wilde, B., Ter Mors, A. W., & Witteveen, C. (2014). Push and rotate: A complete multi-agent pathfinding algorithm.
*Journal of Artificial Intelligence Research*,*51*, 443–492.MathSciNetCrossRefGoogle Scholar - Desaraju, V. R., & How, J. P. (2012). Decentralized path planning for multi-agent teams with complex constraints.
*Autonomous Robots*,*32*(4), 385–403.CrossRefGoogle Scholar - Dixon, C., & Frew, E. W. (2007). Maintaining optimal communication chains in robotic sensor networks using mobility control. In
*International conference on robot communication and coordination*.Google Scholar - Elston, J., Frew, E., Lawrence, D., Gray, P., & Argrow, B. (2009). Net-centric communication and control for a heterogeneous unmanned aircraft system.
*Journal of Intelligent and Robotic Systems*,*56*(1–2), 199–232.CrossRefGoogle Scholar - Ferguson, D., & Stentz, A. (2006). Anytime RRTS. In
*2006 IEEE/RSJ international conference on intelligent robots and systems*(pp. 5369–5375). IEEE.Google Scholar - Ford, K. M., Allen, J., Suri, N., Hayes, P. J., & Morris, R. (2010). PIM: A novel architecture for coordinating behavior of distributed systems.
*AI Magazine*,*31*(2), 9.CrossRefGoogle Scholar - Gammell, J. D., Srinivasa, S. S., & Barfoot, T. D. (2014). Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In
*2014 IEEE/RSJ international conference on intelligent robots and systems (IROS 2014)*(pp. 2997–3004). IEEE.Google Scholar - Goldreich, O. (2011). Finding the shortest move-sequence in the graph-generalized 15-puzzle is NP-hard. In O. Goldreich (Ed.),
*Studies in complexity and cryptography. Miscellanea on the interplay between randomness and computation*. Lecture notes in computer science (Vol. 6650). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar - Holland, O., Woods, J., De Nardi, R., & Clarck, A. (2005). Beyond swarm intelligence: The ultraswarm. In
*IEEE swarm intelligence symposium*.Google Scholar - Hollinger, G., Yerramalli, S., Singh, S., Mitra, U., & Sukhatme, G. (2011). Distributed coordination and data fusion for underwater search. In
*IEEE international conference on robotics and automation*(pp. 349–355).Google Scholar - Hsieh, M. A., Chaimowicz, L., Cowley, A., Grocholsky, B., Keller, J., Kumar, V., et al. (2007). Adaptive teams of autonomous aerial and ground robots for situational awareness.
*Journal of Field Robotics*,*24*(11), 991–1014.CrossRefGoogle Scholar - Hsu, D., Kindel, R., Latombe, J. C., & Rock, S. (2002). Randomized kinodynamic motion planning with moving obstacles.
*The International Journal of Robotics Research*,*21*(3), 233–255.CrossRefGoogle Scholar - Johnson, M., Intlekofer Jr, K., Jung, H., Bradshaw, J. M., Allen, J., Suri, N., et al. (2008). Coordinated operations in mixed teams of humans and robots. In
*Proceedings of the first IEEE conference on distributed human-machine systems*.Google Scholar - Johnson, W. W., Story, W. E., et al. (1879). Notes on the puzzle.
*American Journal of Mathematics*,*2*(4), 397–404.MathSciNetCrossRefGoogle Scholar - Khoo, A., & Horswill, I. (2002). An efficient coordination architecture for autonomous robot teams. In
*IEEE international conference on robotics and automation, 2002. Proceedings. ICRA ’02*(Vol. 1, pp. 287–292).Google Scholar - Kornhauser, D., Miller, G., & Spirakis, P. (1984). Coordinating pebble motion on graphs, the diameter of permutation groups, and applications. In
*25th annual symposium on foundations of computer science*(pp. 241–250). https://doi.org/10.1109/SFCS.1984.715921. - Krontiris, A., Luna, R., & Bekris, K. E. (2013). From feasibility tests to path planners for multi-agent pathfinding. In
*Sixth annual symposium on combinatorial search*.Google Scholar - Loyd, S. (1959).
*Mathematical puzzles of Sam Loyd*. New York: Dover Publications Inc.Google Scholar - Nardi, R. D., Holland, O., Woods, J., & Clark, A. (2006). Swarmav: A swarm of miniature aerial vehicles. Technical Report.Google Scholar
- Otte, M. (2011). Any-Com multi-robot path planning. Ph.D. thesis, University of Colorado at Boulder.Google Scholar
- Otte, M. (2016). Collective cognition & sensing in robotic swarms via an emergent group mind. In
*International symposium on experimental robotics (ISER)*, Tokyo, Japan.Google Scholar - Otte, M., & Correll, N. (2013a).
*Any-Com multi-robot path-planning: Maximizing collaboration for variable bandwidth*(pp. 161–173). Berlin: Springer. https://doi.org/10.1007/978-3-642-32723-0.CrossRefGoogle Scholar - Otte, M., & Correll, N. (2013b). C-FOREST: Parallel shortest-path planning with super linear speedup.
*IEEE Transactions on Robotics*,*29*, 798–806.CrossRefGoogle Scholar - Otte, M., & Correll, N. (2014).
*Any-Com multi-robot path-planning with dynamic teams: Multi-robot coordination under communication constraints*(pp. 743–757). Berlin: Springer. https://doi.org/10.1007/978-3-642-28572-1_51.CrossRefGoogle Scholar - Peasgood, M., McPhee, J., & Clark, C. (2006). Complete and scalable multi-robot planning in tunnel environments.
*IFAC Proceedings Volumes*,*39*(20), 26–31.CrossRefGoogle Scholar - Ratner, D., & Warmuth, M. (1986). Finding a shortest solution for the n\(\times \)n extension of the 15-puzzle is intractable. In
*AAAI*(pp. 168–172).Google Scholar - Rutishauser, S., Correll, N., & Martinoli, A. (2009). Collaborative coverage using a swarm of networked miniature robots.
*Robotics and Autonomous Systems*,*57*(5), 517–525.CrossRefGoogle Scholar - Scerri, P., Owens, S., Yu, B., & Sycara, K. (2007). A decentralized approach to space deconfliction. In
*2007 10th international conference on information fusion*(pp. 1–8). IEEE.Google Scholar - Sharon, G., Stern, R., Felner, A., & Sturtevant, N. R. (2012). Meta-agent conflict-based search for optimal multi-agent path finding. In
*SOCS*.Google Scholar - Sharon, G., Stern, R., Felner, A., & Sturtevant, N. R. (2015). Conflict-based search for optimal multi-agent pathfinding.
*Artificial Intelligence*,*219*, 40–66.MathSciNetCrossRefGoogle Scholar - Solovey, K., & Halperin, D. (2014). k-color multi-robot motion planning.
*The International Journal of Robotics Research*,*33*(1), 82–97.CrossRefGoogle Scholar - Standley, T., & Korf, R. (2011). Complete algorithms for cooperative pathfinding problems. In
*IJCAI*(pp. 668–673).Google Scholar - Suri, N., & Cabri, G. (2014).
*Adaptive, dynamic, and resilient systems*. Boca Raton, FL: CRC Press Taylor & Francis Group.Google Scholar - Suri, N., Marcon, M., Quitadamo, R., Rebeschini, M., Arguedas, M., Stabellini, S., et al. (2008). An adaptive and efficient peer-to-peer service-oriented architecture for manet environments with agile computing. In
*Network operations and management symposium workshops*(pp. 364–371). IEEE.Google Scholar - Suri, N., Rebeschini, M., Breedy, M., Carvalho, M., & Arguedas, M. (2006). Resource and service discovery in wireless ad-hoc networks with agile computing. In
*Military Communications Conference, 2006. MILCOM 2006*(pp. 1–7). IEEE.Google Scholar - Surynek, P. (2009). An application of pebble motion on graphs to abstract multi-robot path planning. In
*21st IEEE international conference on tools with artificial intelligence*(pp. 151–158). IEEE.Google Scholar - Surynek, P. (2014). Solving abstract cooperative path-finding in densely populated environments.
*Computational Intelligence*,*30*(2), 402–450.MathSciNetCrossRefGoogle Scholar - Sutton, D. J., Klein, P., Otte, M., & Correll, N. (2010). Object interaction language (oil): An intent-based language for programming self-organized sensor/actuator networks. In
*IEEE/RSJ international conference on intelligent robots and systems (IROS)*.Google Scholar - van Den Berg, J., Snoeyink, J., Lin, M. C., & Manocha, D. (2009). Centralized path planning for multiple robots: Optimal decoupling into sequential plans. In
*RSS*.Google Scholar - Voyles, R. M., Bae, J., Larson, A. C., & Ayad, M. A. (2009). Wireless video sensor networks for sparse, resource-constrained, multi-robot teams.
*Intelligent Service Robotics*,*2*(4), 235–246.CrossRefGoogle Scholar - Voyles, R., Povilus, S., Mangharam, R., & Li, K. (2010). Reconode: A reconfigurable node for heterogeneous multi-robot search and rescue. In
*IEEE international workshop on safety, security and rescue robotics*.Google Scholar - Wagner, G., & Choset, H. (2015). Subdimensional expansion for multirobot path planning.
*Artificial Intelligence*,*219*, 1–24.MathSciNetCrossRefGoogle Scholar - Wagner, G., Kang, M., & Choset, H. (2012). Probabilistic path planning for multiple robots with subdimensional expansion. In
*2012 IEEE international conference on robotics and automation (ICRA)*(pp. 2886–2892). IEEE.Google Scholar - Wedge, N. A., & Branicky, M. S. (2008). On heavy-tailed runtimes and restarts in rapidly-exploring random trees. In
*AAAI Conference on artificial intelligence*.Google Scholar - Wilson, R. M. (1974). Graph puzzles, homotopy, and the alternating group.
*Journal of Combinatorial Theory, Series B*,*16*(1), 86–96.MathSciNetCrossRefGoogle Scholar - Yu, J., & Rus, D. (2015). Pebble motion on graphs with rotations: Efficient feasibility tests and planning algorithms. In H. L Akin, N. M. Amato, V. Isler, & A. F van der Stappen (Eds.),
*Algorithmic foundations of robotics XI*(pp. 729–746). Springer.Google Scholar