Skip to main content
Springer Nature Link
Log in

Decentralized path planning for multi-agent teams with complex constraints

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

This paper presents a novel approach to address the challenge of planning paths for multi-agent systems subject to complex constraints. The technique, called the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT) algorithm, extends the Closed-loop RRT (CL-RRT) algorithm to handle multiple agents while retaining its ability to plan quickly. A core component of the DMA-RRT algorithm is a merit-based token passing coordination strategy that makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents replan. The reordering is based on a measure of each agent’s incentive to change the plan and allows agents with a greater potential improvement to replan sooner, which is demonstrated to improve the team’s overall performance compared to a traditional, scripted replan order. The main contribution of the work is a version of the algorithm, called Cooperative DMA-RRT, which introduces a cooperation strategy that allows an agent to modify its teammates’ plans in order to select paths that reduce their combined cost. This modification further improves team performance and avoids certain common deadlock scenarios. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and numerous simulation and experimental results are presented to demonstrate their performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Algorithm 3
Fig. 2
Algorithm 4
Algorithm 5
Fig. 3
Algorithm 6
Algorithm 7
Algorithm 8
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. The arrow is cyan in this case since one of the other agents has the token. If agent 1 were the current token holder, its arrow would be green instead.

References

  • Amir, Y., Moser, L., Melliar-Smith, P., Agarwal, D., & Ciarfella, P. (1993). Fast message ordering and membership using a logical token-passing ring. In Proceedings the 13th international conference on distributed computing systems (pp. 551–560). New York: IEEE Press.

    Chapter  Google Scholar 

  • Aoude, G. S., Luders, B. D., Levine, D. S., & How, J. P. (2010). Threat-aware path planning in uncertain urban environments. In IEEE/RSJ international conference on intelligent robots and systems (IROS), Taipei, Taiwan (pp. 6058–6063).

    Google Scholar 

  • Ayanian, N., & Kumar, V. (2010). Decentralized feedback controllers for multiagent teams in environments with obstacles. IEEE Transactions on Robotics, 26(5), 878–887.

    Article  Google Scholar 

  • Azarm, K., & Schmidt, G. (1997). Conflict-free motion of multiple mobile robots based on decentralized motion planning and negotiation. In IEEE international conference on robotics and automation (Vol. 4, pp. 3526–3533).

    Google Scholar 

  • Banerjee, S., & Chrysanthis, P. (1996). A new token passing distributed mutual exclusion algorithm. In Proceedings of the 16th international conference on distributed computing systems (pp. 717–724). New York: IEEE Press.

    Chapter  Google Scholar 

  • Barraquand, J., Langlois, B., & Latombe, J. C. (1992). Numerical potential field techniques for robot path planning. IEEE Transactions on Systems, Man, and Cybernetics, 22(2), 224–241.

    Article  MathSciNet  Google Scholar 

  • Basar, T. (1988). Asynchronous algorithms in non-cooperative games. Journal of Economic Dynamics and Control, 12(1), 167–172.

    Article  MATH  Google Scholar 

  • Bertsekas, D., & Gallager, R. (1992). Data networks. Englewood Cliffs: Prentice-Hall.

    MATH  Google Scholar 

  • Chun, L., Zheng, Z., & Chang, W. (1999). A decentralized approach to the conflict-free motion planning for multiple mobile robots. In IEEE international conference on robotics and automation (ICRA) (Vol. 2, pp. 1544–1549).

    Google Scholar 

  • Desaraju, V., Ro, H. C., Yang, M., Tay, E., Roth, S., & Del Vecchio, D. (2009). Partial order techniques for vehicle collision avoidance: application to an autonomous roundabout test-bed. In IEEE international conference on robotics and automation (ICRA) (pp. 82–87).

    Chapter  Google Scholar 

  • Dunbar, W., & Murray, R. (2006). Distributed receding horizon control for multi-vehicle formation stabilization. Automatica, 42(4), 549–558.

    Article  MathSciNet  MATH  Google Scholar 

  • Erdmann, M., & Lozano-Pérez, T. (1987). On multiple moving objects. Algorithmica, 2, 477–521.

    Article  MathSciNet  MATH  Google Scholar 

  • Frazzoli, E., Dahleh, M. A., & Feron, E. (2002). Real-time motion planning for agile autonomous vehicles. Journal of Guidance, Control, and Dynamics, 25(1), 116–129.

    Article  Google Scholar 

  • Hoffmann, G., & Tomlin, C. (2008). Decentralized cooperative collision avoidance for acceleration constrained vehicles. In IEEE conference on decision and control (CDC) (pp. 4357–4363).

    Google Scholar 

  • How, J. P., Bethke, B., Frank, A., Dale, D., & Vian, J. (2008). Real-time indoor autonomous vehicle test environment. IEEE Control Systems Magazine, 28(2), 51–64.

    Article  MathSciNet  Google Scholar 

  • Inalhan, G., Stipanovic, D., & Tomlin, C. (2002). Decentralized optimization, with application to multiple aircraft coordination. In IEEE conference on decision and control (CDC), Las Vegas, NV.

    Google Scholar 

  • Jager, M., & Nebel, B. (2001). Decentralized collision avoidance, deadlock detection, and deadlock resolution for multiple mobile robots. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (Vol. 3, pp. 1213–1219).

    Google Scholar 

  • Johnson, E., Tang, Z., Balakrishnan, M., Rubio, J., Zhang, H., & Sreepuram, S. (2003). Robust token management for unreliable networks. In Military communications conference. MILCOM 2003 (Vol. 1, pp. 399–404). New York: IEEE Press.

    Chapter  Google Scholar 

  • Karaman, S., & Frazzoli, E. (2010). Incremental sampling-based algorithms for optimal motion planning. In Robotics: science and systems (RSS).

    Google Scholar 

  • Karaman, S., Walter, M., Perez, A., Frazzoli, E., & Teller, S. (2011). Anytime motion planning using the rrt*. In Proceedings of the IEEE international conference on robotics and automation, institute of electrical and electronics engineers.

    Google Scholar 

  • Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.

    Article  Google Scholar 

  • Keviczky, T., Borrelli, F., & Balas, G. J. (2004). A study on decentralized receding horizon control for decoupled systems. In American control conference (ACC), Boston, MA (pp. 4921–4926).

    Google Scholar 

  • Khatib, O. (1985). Real-time obstacle avoidance for manipulators and mobile robots. In IEEE international conference on robotics and automation (Vol. 2, pp. 500–505).

    Google Scholar 

  • Kim, J., & Kim, C. (1997). A total ordering protocol using a dynamic token-passing scheme. Distributed Systems Engineering, 4, 87.

    Article  Google Scholar 

  • Koenig, S., & Likhachev, M. (2002). D ∗ lite. In Proceedings AAAI national conference on artificial intelligence (pp. 476–483).

    Google Scholar 

  • Koenig, S., Likhachev, M., & Furcy, D. (2004). Lifelong planning A*. Artificial Intelligence, 155(1–2), 93–146.

    Article  MathSciNet  MATH  Google Scholar 

  • Koren, Y., & Borenstein, J. (1991). Potential field methods and their inherent limitations for mobile robot navigation. In IEEE international conference on robotics and automation (pp. 1398–1404).

    Google Scholar 

  • Kuwata, Y., & How, J. P. (2011). Cooperative distributed robust trajectory optimization using receding horizon MILP. IEEE Transactions on Control Systems Technology, 19(2), 423–431.

    Article  Google Scholar 

  • Kuwata, Y., Richards, A., Schouwenaars, T., & How, J. P. (2007). Distributed robust receding horizon control for multivehicle guidance. IEEE Transactions on Control Systems Technology, 15(4), 627–641.

    Article  Google Scholar 

  • Kuwata, Y., Teo, J., Karaman, S., Fiore, G., Frazzoli, E., & How, J. P. (2008). Motion planning in complex environments using closed-loop prediction. In AIAA guidance, navigation, and control conference (GNC) (AIAA-2008-7166), Honolulu, HI.

    Google Scholar 

  • Kuwata, Y., Teo, J., Fiore, G., Karaman, S., Frazzoli, E., & How, J. P. (2009). Real-time motion planning with applications to autonomous urban driving. IEEE Transactions on Control Systems Technology, 17(5), 1105–1118.

    Article  Google Scholar 

  • LaValle, S. M. (1998). Rapidly-exploring random trees: a new tool for path planning (Tech. Rep. 98–11). Iowa State University.

  • LaValle, S. M. (2006). Planning algorithms. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Lee, J., Nam, H., & Lyou, J. (1995). A practical collision-free trajectory planning for two robot systems. In Proceedings of the IEEE international conference on robotics and automation (Vol. 3, pp. 2439–2444). New York: IEEE Press.

    Google Scholar 

  • Leonard, N., Paley, D., Lekien, F., Sepulchre, R., Fratantoni, D., & Davis, R. (2007). Collective motion, sensor networks, and ocean sampling. Proceedings of the IEEE, 95(1), 48–74.

    Article  Google Scholar 

  • Leonard, J., How, J. P., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., Koch, O., Kuwata, Y., Moore, D., Olson, E., Peters, S., Teo, J., Truax, R., Walter, M., Barrett, D., Epstein, A., Maheloni, K., Moyer, K., Jones, T., Buckley, R., Antone, M., Galejs, R., Krishnamurthy, S., & Williams, J. (2008). A perception-driven autonomous urban vehicle. Journal of Field Robotics, 25(10), 727–774.

    Article  Google Scholar 

  • Likhachev, M., & Stentz, A. (2008). R* search. In Proceedings of the AAAI conference on artificial intelligence (pp. 344–350).

    Google Scholar 

  • Luders, B., Karaman, S., Frazzoli, E., & How, J. P. (2010). Bounds on track error using closed-loop rapidly-exploring random trees. In American control conference (ACC), Baltimore (pp. 5406–5412).

    Google Scholar 

  • Mueller, F. (2001). Fault tolerance for token-based synchronization protocols. In Proceedings 15th international parallel and distributed processing symposium (pp. 1257–1264). New York: IEEE Press.

    Chapter  Google Scholar 

  • Murray, R. (2007). Recent research in cooperative control of multi-vehicle systems. ASME Journal of Dynamic Systems, Measurement, and Control, 129(5), 571–583.

    Article  Google Scholar 

  • Narváez, P., Siu, K., & Tzeng, H. (2000). New dynamic algorithms for shortest path tree computation. IEEE/ACM Transactions on Networking, 8(6), 734–746.

    Article  Google Scholar 

  • Olfati-Saber, R., Fax, J., & Murray, R. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233.

    Article  Google Scholar 

  • Pallottino, L., Scordio, V., & Bicchi, A. (2004). Decentralized cooperative conflict resolution among multiple autonomous mobile agents. In IEEE conference on decision and control (CDC) (Vol. 5, pp. 4758–4763).

    Google Scholar 

  • Parker, L. (2002). Distributed algorithms for multi-robot observation of multiple moving targets. Autonomous Robots, 12(3), 231–255.

    Article  MATH  Google Scholar 

  • Purwin, O., D’Andrea, R., & Lee, J. (2008). Theory and implementation of path planning by negotiation for decentralized agents. Robotics and Autonomous Systems, 56(5), 422–436.

    Article  Google Scholar 

  • Richards, A. G. (2005). Robust constrained model predictive control. Ph.D. thesis, Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA.

  • Richards, A., Schouwenaars, T., How, J. P., & Feron, E. (2002). Spacecraft trajectory planning with avoidance constraints using mixed-integer linear programming. Journal of Guidance, Control, and Dynamics, 25(4), 755–764.

    Article  Google Scholar 

  • Sae-Hau, C. (2003). Multi-vehicle rover testbed using a new indoor positioning sensor. S.M. thesis draft, Massachusetts Institute of Technology.

  • Scerri, P., Owens, S., Yu, B., & Sycara, K. (2007). A decentralized approach to space deconfliction. In Proc. 10th int. information fusion conf. (pp. 1–8).

    Google Scholar 

  • Schouwenaars, T., How, J., & Feron, E. (2004). Decentralized cooperative trajectory planning of multiple aircraft with hard safety guarantees. In AIAA guidance, navigation, and control conference (GNC), Providence, RI.

    Google Scholar 

  • Si, J. (2004). Handbook of learning and approximate dynamic programming (Vol. 2). New York: Wiley–IEEE Press.

    Book  Google Scholar 

  • Swigart, J., & Lall, S. (2010). An explicit dynamic programming solution for a decentralized two-player optimal linear-quadratic regulator. In Proceedings of mathematical theory of networks and systems.

    Google Scholar 

  • Tanner, H., Loizou, S., & Kyriakopoulos, K. (2001). Nonholonomic stabilization with collision avoidance for mobile robots. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (Vol. 3, pp. 1220–1225). New York: IEEE Press.

    Google Scholar 

  • Teller, S., Walter, M. R., Antone, M., Correa, A., Davis, R., Fletcher, L., Frazzoli, E., Glass, J., How, J. P., Huang, A. S., Jeon, J., Karaman, S., Luders, B., Roy, N., & Sainath, T. (2010). A voice-commanded robotic forklift working alongside humans in minimally-prepared outdoor environments. In Proceedings of the IEEE international conference on robotics and automation, Anchorage, AK.

    Google Scholar 

  • Trodden, P. (2009). Robust distributed control of constrained linear systems. Ph.D. thesis, University of Bristol.

  • Trodden, P., & Richards, A. (2006). Robust distributed model predictive control using tubes. In Proceedings of the American control conference, Minneapolis, MN (pp. 2034–2039).

    Google Scholar 

  • van den Berg, J., Snoeyink, J., Lin, M., & Manocha, D. (2009). Centralized path planning for multiple robots: optimal decoupling into sequential plans. In Proceedings of robotics: science and systems, Seattle, USA.

    Google Scholar 

  • Velagapudi, P., Sycara, K., & Scerri, P. (2010). Decentralized prioritized planning in large multirobot teams. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4603–4609).

    Google Scholar 

  • Venkat, A. N., Rawlings, J. B., & Wright, S. J. (2005). Stability and optimality of distributed model predictive control. In Proceedings of the IEEE conference on decision and control, Seville, Spain (pp. 6680–6685).

    Chapter  Google Scholar 

  • Wang, P., & Zhuang, W. (2008). A token-based scheduling scheme for wlans supporting voice/data traffic and its performance analysis. IEEE Transactions on Wireless Communications, 7(5), 1708–1718.

    Article  Google Scholar 

  • Yershova, A., Jaillet, L., Siméon, T., & LaValle, S. M. (2005). Dynamic-domain RRTs: Efficient exploration by controlling the sampling domain. In IEEE international conference on robotics and automation (ICRA), Barcelona, Spain (pp. 3856–3861).

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank Brandon Luders and Georges Aoude for their contributions to this research effort. Research funded in part by the Office of Secretary of Defense under Air Force Contract FA8721-05-C-0002.

Author information

Authors and Affiliations

  1. Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA

    Vishnu R. Desaraju & Jonathan P. How

Authors
  1. Vishnu R. Desaraju
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jonathan P. How
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Vishnu R. Desaraju.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Desaraju, V.R., How, J.P. Decentralized path planning for multi-agent teams with complex constraints. Auton Robot 32, 385–403 (2012). https://doi.org/10.1007/s10514-012-9275-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-012-9275-2

Keywords