Autonomous Robots

, Volume 37, Issue 1, pp 1–25 | Cite as

Online decentralized information gathering with spatial–temporal constraints

Article

Abstract

We are interested in coordinating a team of autonomous mobile sensor agents in performing a cooperative information gathering task while satisfying mission-critical spatial–temporal constraints. In particular, we present a novel set of constraint formulations that address inter-agent collisions, collisions with static obstacles, network connectivity maintenance, and temporal-coverage in a resource-efficient manner. These constraints are considered in the context of the target search problem, where the team plans trajectories that maximize the probability of target detection. We model constraints continuously along the agents’ trajectories and integrate these constraint models into decentralized team planning using a computationally efficient solution method based on the Lagrangian formulation and decentralized optimization. We validate our approach in simulation with five UAVs performing search, and through hardware experiments with four indoor mobile robots. Our results demonstrate team planning with spatial–temporal constraints that preserves the performance of unconstrained information gathering and is feasible to implement with reasonable computational and communication resources.

Keywords

Team planning Decentralized optimization Information gathering Spatial–temporal constraints Collision avoidance Network connectivity  Temporal-coverage 

References

  1. Ahmadzadeh, A., Jadbabaie, A., Kumar, V. & Pappas, G. (2006). Multi-UAV cooperative surveillance with spatio-temporal specifications. In Proceedings of IEEE CDC.Google Scholar
  2. Antonelli, G., Chiaverini, S. & Marino, A. (2012). A coordination strategy for multi-robot sampling of dynamic fields. In Proceedings of IEEE ICRA.Google Scholar
  3. Ayanian, N., & Kumar, V. (2010). Decentralized feedback controllers for multiagent teams in environments with obstacles. IEEE Transactions on Robotics, 26(5), 878–887.CrossRefGoogle Scholar
  4. Barry, A., Majumdar, A. & Tedrake, R. (2012). Safety verification of reactive controllers for UAV flight in cluttered environments using barrier certificates. In Proceedings of IEEE ICRA.Google Scholar
  5. Bertsekas, D. (1996). Constrained optimization and Lagrange multiplier methods. Belmont, MA: Athena Scientific.Google Scholar
  6. Bhattacharya, S., Kumar, V. & Likhachev, M. (2010). Distributed optimization with pairwise constraints and its application to multi-robot path planning. In Proceedings of RSS.Google Scholar
  7. Bretl, T. (2012). Minimum-time optimal control of many robots that move in the same direction at different speeds. IEEE Transactions on Robotics, 28(2), 351–363.CrossRefGoogle Scholar
  8. Casbeer, D., Kingston, D., Beard, R., & McLain, T. (2006). Cooperative forest fire surveillance using a team of small unmanned air vehicles. International Journal of Systems Science, 37(6), 351–360.CrossRefMATHGoogle Scholar
  9. Chung, T. & Burdick, J. (2007). A decision-making framework for control strategies in probabilistic search. In Proceedings of IEEE ICRA.Google Scholar
  10. Cole, D., Göktoǧan, A., & Sukkarieh, S. (2008). The demonstration of a cooperative control architecture for UAV teams. Experimental Robotics, 39, 501–510.CrossRefGoogle Scholar
  11. Desaraju, V., & How, J. (2012). Decentralized path planning for multi-agent teams with complex constraints. Autonomous Robots, 32(4), 385–403.CrossRefGoogle Scholar
  12. Dimarogonas, D., Kyriakopoulos, K. & Theodorakatos, D. (2006). Totally distributed motion control of sphere world multi-agent systems using decentralized navigation functions. In Proceedings of IEEE ICRA.Google Scholar
  13. Durham, J., Carli, R., Frasca, P., & Bullo, F. (2012). Discrete partitioning and coverage control for gossiping robots. IEEE Transactions on Robotics, 28(2), 364–378.CrossRefGoogle Scholar
  14. Frew, E., Lawrence, D., & Morris, S. (2008). Coordinated standoff tracking of moving targets using lyapunov guidance vector fields. Journal of Guidance, Control, and Dynamics, 31(2), 290–306.CrossRefGoogle Scholar
  15. Furukawa, T., Bourgault, F., Lavis, B. & Durrant-Whyte, H. (2006). Recursive bayesian search-and-tracking using coordinated UAVs for lost targets. In Proceedings of IEEE ICRA.Google Scholar
  16. Gan, S. & Sukkarieh, S. (2011). Multi-UAV target search using explicit decentralized gradient-based negotiation. In Proceedings of IEEE ICRA.Google Scholar
  17. Gan, S., Fitch, R. & Sukkarieh, S. (2012). Real-time decentralized search with inter-agent collision avoidance. In Proceedings of IEEE ICRA.Google Scholar
  18. Gillula, J., Hoffmann, G., Huang, H., Vitus, M., & Tomlin, C. (2011). Applications of hybrid reachability analysis to robotic aerial vehicles. International Journal of Robotics Research, 30(3), 335–354.CrossRefGoogle Scholar
  19. Grocholsky, B., Makarenko, A. & Durrant-Whyte, H. (2003). Information-theoretic coordinated control of multiple sensor platforms. In Proceedings of IEEE ICRA.Google Scholar
  20. Hoffmann, G., & Tomlin, C. (2010). Mobile sensor network control using mutual information methods and particle filters. IEEE Transactions on Automatic Control, 55(1), 32–47.CrossRefMathSciNetGoogle Scholar
  21. Hollinger, G. & Singh, S. (2010). Multi-robot coordination with periodic connectivity. In Proceedings of IEEE ICRA.Google Scholar
  22. Hollinger, G., Singh, S., Djugash, J., & Kehagias, A. (2009). Efficient multi-robot search for a moving target. International Journal of Robotics Research, 28(2), 201–219.CrossRefGoogle Scholar
  23. How, J. & King, E. (2004). Flight demonstrations of cooperative control for UAV teams. AIAA 3rd Unmanned Unlimited Technical Conf Workshop and Exhibit, Chicago.Google Scholar
  24. Inalhan, G. (2004). Decentralized optimization across independent decision makers with incomplete models. PhD thesis, Stanford University.Google Scholar
  25. Julian, B., Angermann, M., Schwager, M., & Rus, D. (2012). Distributed robotic sensor networks: An information-theoretic approach. International Journal of Robotics Research, 31(10), 1134–1154.CrossRefGoogle Scholar
  26. Kingston, D., Beard, R., & Holt, R. (2008). Decentralized perimeter surveillance using a team of UAVs. IEEE Transactions on Robotics, 24(6), 1394–1404.CrossRefGoogle Scholar
  27. Kovacina, M., Palmer, D., Yang, G. & Vaidyanathan, R. (2002). Multi-agent control algorithms for chemical cloud detection and mapping using unmanned air vehicles. In Proceedings of IEEE/RSJ IROS.Google Scholar
  28. Kuwata, Y. & How, J. (2006). Decentralized cooperative trajectory optimization for UAVs with coupling constraints. In Proceedings of IEEE CDC.Google Scholar
  29. Lal, R. & Fitch, R. (2009). A hardware-in-the-loop simulator for distributed robotics. In Proceedings of ARAA ACRA.Google Scholar
  30. Lapierre, L., & Zapata, R. (2012). A guaranteed obstacle avoidance guidance system. Autonomous Robots, 32(3), 177–187. Google Scholar
  31. Leung, C., Huang, S., Kwok, N., & Dissanayake, G. (2006). Planning under uncertainty using model predictive control for information gathering. Robotics and Autonomous Systems, 54(11), 898–910.Google Scholar
  32. Mathews, G., Durrant-Whyte, H., & Prokopenko, M. (2009). Decentralised decision making in heterogeneous teams using anonymous optimisation. Robotics and Autonomous Systems, 57(3), 310–320.CrossRefGoogle Scholar
  33. Raffard, R., Tomlin, C. & Boyd, S. (2004). Distributed optimization for cooperative agents: application to formation flight. In Proceedings of IEEE CDC.Google Scholar
  34. Renzaglia, A., Doitsidis, L., Martinelli, A., & Kosmatopoulos, E. (2012). Multi-robot three-dimensional coverage of unknown areas. International Journal of Robotics Research, 31(6), 738–752.CrossRefGoogle Scholar
  35. Sabattini, L., Secchi, C. & Chopra, N. (2012). Decentralized connectivity maintenance for networked lagrangian dynamical systems. In Proceedings of IEEE ICRA.Google Scholar
  36. Schouwenaars, T., How, J. & Feron, E. (2004). Decentralized cooperative trajectory planning of multiple aircraft with hard safety guarantees. In Proceedings of AIAA GNC.Google Scholar
  37. Tang, Z., & Ozguner, U. (2005). Motion planning for multitarget surveillance with mobile sensor agents. IEEE Transactions on Robotics, 21(5), 898–908.CrossRefGoogle Scholar
  38. Tanner, H., & Christodoulakis, D. (2007). Decentralized cooperative control of heterogeneous vehicle groups. Robotics and Autonomous Systems, 55(11), 811–823.CrossRefGoogle Scholar
  39. Tisdale, J., Kim, Z., & Hedrick, J. (2009). Autonomous UAV path planning and estimation. IEEE Robotics and Automation Magazine, 16(2), 35–42.CrossRefGoogle Scholar
  40. Wong, E., Bourgault, F. & Furukawa, T. (2005). Multi-vehicle bayesian search for multiple lost targets. In Proceedings of IEEE ICRA.Google Scholar
  41. Wu, A., & How, J. (2012). Guaranteed infinite horizon avoidance of unpredictable, dynamically constrained obstacles. Autonomous Robots, 32(3), 227–242.CrossRefMathSciNetGoogle Scholar
  42. Yang, K., Gan, S., & Sukkarieh, S. (2010). An efficient path planning and control algorithm for RUAVs in unknown and cluttered environments. Journal of Intelligent and Robotic Systems, 57(1), 101–122.CrossRefMATHGoogle Scholar
  43. Zavlanos, M., & Pappas, G. (2007). Potential fields for maintaining connectivity of mobile networks. IEEE Transactions on Robotics, 23(4), 812–816.CrossRefGoogle Scholar
  44. Zavlanos, M., & Pappas, G. (2008). Distributed connectivity control of mobile networks. IEEE Transactions on Robotics, 24(6), 1416–1428.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Australian Centre for Field Robotics (ACFR)The University of SydneyDarlingtonAustralia

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