Skip to main content

Real-time adaptive multi-robot exploration with application to underwater map construction

Abstract

This paper deals with the problem of autonomous exploration of unknown areas using teams of Autonomous X Vehicles (AXVs)—with X standing for Aerial, Underwater or Sea-surface—where the AXVs have to autonomously navigate themselves so as to construct an accurate map of the unknown area. Such a problem can be transformed into a dynamic optimization problem which, however, is NP-complete and thus infeasible to be solved. A usual attempt is to relax this problem by employing greedy (optimal one-step-ahead) solutions which may end-up quite problematic. In this paper, we first show that optimal one-step-ahead exploration schemes that are based on a transformed optimization criterion can lead to highly efficient solutions to the multi-AXV exploration. Such a transformed optimization criterion is constructed using both theoretical analysis and experimental investigations and attempts to minimize the “disturbing” effect of deadlocks and nonlinearities to the overall exploration scheme. As, however, optimal one-step-ahead solutions to the transformed optimization criterion cannot be practically obtained using conventional optimization schemes, the second step in our approach is to combine the use of the transformed optimization criterion with the cognitive adaptive optimization (CAO): CAO is a practicably feasible computational methodology which adaptively provides an accurate approximation of the optimal one-step-ahead solutions. The combination of the transformed optimization criterion with CAO results in a multi-AXV exploration scheme which is both practically implementable and provides with quite efficient solutions as it is shown both by theoretical analysis and, most importantly, by extensive simulation experiments and real-life underwater sea-floor mapping experiments in the Leixes port, Portugal.

This is a preview of subscription content, access via your institution.

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

Notes

  1. 1.

    The problem of multi-robot trajectory generation for maximizing SLAM efficiency is also referred in the literature as exploration or optimal motion strategy. In the rest of this paper, these terms will be used interchangeably.

  2. 2.

    For simplicity, we assume that the orientation of the AXVs is fixed and constant all the time. All the results of this paper can be easily extended in the case where the orientation changes by the navigation algorithm.

  3. 3.

    Additionally, it might be useful to set an upper limit (big enough) in the times that a landmark can be estimated by at least one AXV with any accuracy. This limit will serve as deadlock avoidance meachnism in cases of a landmark cannot be accurately estimated, due to the local morphology of the area to be mapped. We would like to thank one of the reviewers who pointed that out.

  4. 4.

    Table 1 presents the performance using the average percentage of the Non-Accurately estimated landmarks, so as to be in-line with the upcoming results.

  5. 5.

    According to Kosmatopoulos (2009) and Kosmatopoulos and Kouvelas (2009) it suffices to choose N to be any positive integer larger or equal to \(2\times \)[the number of variables being optimized by CAO]. In our case the variables optimized are the robot positions \(\mathbf{X}^R_{t_i}\) and thus it suffices for N to satisfy \(N\ge 2N_R\times \dim \left( \mathbf{X}^R_{t_i}\right) \).

  6. 6.

    A video footage of this experiment can be found on https://www.youtube.com/watch?v=menK5tMRw-s.

  7. 7.

    Please note that both interpolated versions of usual practice present some ridges along the constructed terrain. These ridges correspond to the areas where the AUVs traversed and therefore the samples’ concentration is greater than the rest of the terrain.

  8. 8.

    In order to implement this, at first we discretize, with a sufficient small step, the areas to be compared and afterwords we apply the \(L^2\)-Norm on the vectorized versions of the sampled areas.

References

  1. Birk, A., Pfingsthorn, M., & Bülow, H. (2012). Advances in underwater mapping and their application potential for safety, security, and rescue robotics. In IEEE International Symposium on Safety, Security, Rescue Robotics (SSRR). IEEE Press.

  2. Reed, S., Wood, J., & Hawort, C. (2010). The detection and disposal od ied devices within harbor regions using auvs, smart rovs and data processing/fusion technology. In 2010 international Waterside Security Conference (WSS), (pp. 1–7).

  3. Rodningsby, A., & Bar-Shalom, Y. (2009). Tracking of divers using probabilistic data association filter with a bubble model. IEEE Transactions on Aerospace and Electronic Systems, 45(3), 1181–1193.

    Article  Google Scholar 

  4. Kessel, R. T., & Hollett, R. D. (2006). Underwater intruder detection sonar for protection: State of the art review and implementations. In IEEE International Conference on Technologies for Homeland Security and Safety.

  5. Murphy, R., Steimle, E., Hall, M., Lindemuth, D., Trejo, D., Hurlebaus, Z., Medina-Catina, Z., & Slocum, D. (2009). Robot-assisted bridge inspection after hurricane ike. In 2009 International Workshop on Safety, Security and Rescue Robotics (SSRR), (pp. 1–5).

  6. Roman, C., & Mather, R. (2010). Autonomous underwater vehicles as tools for deep-submergence archaeology. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the the Maritime Environment, 224, 327–340.

    Google Scholar 

  7. DeVault, J. (2000). Robotic system for underwater inspection of bridge piers. IEEE Instrumentation and Measurement Magazine, 3(3), 32–37.

    Article  Google Scholar 

  8. Blondel, P. (2008). A review of acoustic techniques for habitat mapping. Hydroacoustics, 11, 29–38.

    Google Scholar 

  9. Khurshid, J., & Bing-rong, H. (2004). Military robots—a glimpse from today and tomorrow. In ICARCV 2004 8th International Conference on Control, Automation, Robotics and Vision Conference, 2004, (Vol. 1, pp. 771–777).

  10. Samad, A. M., Kamarulzaman, N., Hamdani, M. A., Mastor, T. A., & Hashim, K. A. (2013). The potential of unmanned aerial vehicle (uav) for civilian and mapping application. In 2013 IEEE 3rd International Conference on System Engineering and Technology (ICSET), (pp. 313–318).

  11. Kosmatopoulos, E. B., Papageorgiou, M., Vakouli, A., & Kouvelas, A. (2007). Adaptive fine-tuning of nonlinear control systems with application to the urban traffic control strategy tuc. IEEE Transactions on Control Systems Technology, 15(6), 991–1002.

    Article  Google Scholar 

  12. Kosmatopoulos, E. B. (2009). An adaptive optimization scheme with satisfactory transient performance. Automatica, 45(3), 716–723.

    MathSciNet  Article  MATH  Google Scholar 

  13. Kosmatopoulos, E. B., & Kouvelas, A. (2009). Large-scale nonlinear control system fine-tuning through learning. IEEE Transactions Neural Networks, 20(6), 1009–1023.

    Article  Google Scholar 

  14. Renzaglia, A., Doitsidis, L., Martinelli, A., & Kosmatopoulos, E. B. (2012). Multi-robot three-dimensional coverage of unknown areas. The International Journal of Robotics Research, 31(6), 738–752.

    Article  Google Scholar 

  15. Martijn, N. (2007). Rooker and Andreas Birk. Multi-robot exploration under the constraints of wireless networking. Control Engineering Practice, 15(4), 435–445.

    Article  Google Scholar 

  16. Pfingsthorn, M., Birk, A., & Bulow, H. (2010). An efficient strategy for data exchange in multi-robot mapping under underwater communication constraints. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 4886–4893). IEEE.

  17. Johnson, B., Hallin, N., Leidenfrost, H., O’Rourke, M., & Edwards, D.. (2009). Collaborative mapping with autonomous underwater vehicles in low-bandwidth conditions. In OCEANS 2009-EUROPE, (pp. 1–7). IEEE.

  18. Rajala, A., & Edwards, D. (2007). Allocating auvs for mine map development in mcm. IEEE.

  19. Donald, Bruce, Xavier, Patrick, Canny, John, & Reif, John. (1993). Kinodynamic motion planning. Journal of the ACM (JACM), 40(5), 1048–1066.

    MathSciNet  Article  MATH  Google Scholar 

  20. Pasqualetti, Fabio, Franchi, Antonio, & Bullo, Francesco. (2012). On cooperative patrolling: Optimal trajectories, complexity analysis, and approximation algorithms. IEEE Transactions on Robotics, 28(3), 592–606.

    Article  Google Scholar 

  21. Ny Le J., & Pappas, G. J. (2009). On trajectory optimization for active sensing in gaussian process models. In Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009, (pp. 6286–6292). IEEE.

  22. Milam, M. B., Mushambi, K., & Murray, R. M. (2000). A new computational approach to real-time trajectory generation for constrained mechanical systems. In Proceedings of the 39th IEEE Conference on Decision and Control, 2000, (Vol. 1, pp. 845–851). IEEE.

  23. Kelly, Alonzo, & Nagy, Bryan. (2003). Reactive nonholonomic trajectory generation via parametric optimal control. The International Journal of Robotics Research, 22(7–8), 583–601.

    Article  Google Scholar 

  24. Low, K. H., Dolan, J. M., & Khosla, P. (2011). Active markov information-theoretic path planning for robotic environmental sensing. In The 10th International Conference on Autonomous Agents and Multiagent Systems, Vol. 2, (pp. 753–760). International Foundation for Autonomous Agents and Multiagent Systems.

  25. Tabuada, Paulo, & Pappas, George J. (2005). Hierarchical trajectory refinement for a class of nonlinear systems. Automatica, 41(4), 701–708.

    MathSciNet  Article  MATH  Google Scholar 

  26. Msechu, Eric  J, Roumeliotis, Stergios  I, Ribeiro, Alejandro, & Giannakis, Georgios  B. (2008). Decentralized quantized kalman filtering with scalable communication cost. IEEE Transactions on Signal Processing, 56(8), 3727–3741.

    MathSciNet  Article  Google Scholar 

  27. Zhou, Ke, & Roumeliotis, Stergios  I. (2011). Multirobot active target tracking with combinations of relative observations. IEEE Transactions on Robotics, 27(4), 678–695.

    Article  Google Scholar 

  28. Feder, Hans Jacob  S, Leonard, John  J, & Smith, Christopher  M. (1999). Adaptive mobile robot navigation and mapping. The International Journal of Robotics Research, 18(7), 650–668.

    Article  Google Scholar 

  29. Bourgault, F., Makarenko, A., Williams, S. B., Grocholsky, B., & Durrant-Whyte, H. F. (2002). Information based adaptive robotic exploration. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, (Vol. 1, pp. 540–545). IEEE.

  30. Stachniss, C., & Burgard, W. (2003). Exploring unknown environments with mobile robots using coverage maps. In Proceedings of the International Conference on Artificial Intelligence (IJCAI).

  31. Spletzer, J. R., & Taylor, C. J. (2003). Dynamic sensor planning and control for optimally tracking targets. The International Journal of Robotics Research, 22(1), 7–20.

    Article  Google Scholar 

  32. Beard, Randal  W, McLain, Timothy  W, Goodrich, Michael  A, & Anderson, Erik  P. (2002). oordinated target assignment and intercept for unmanned air vehicles. IEEE Transactions on Robotics and Automation, 18(6), 911–922.

    Article  Google Scholar 

  33. Nesterov, Y. (2007). Gradient methods for minimizing composite objective function. CORE Discussion Papers 2007076, Universit catholique de Louvain, Center for Operations Research and Econometrics (CORE).

  34. Rathnam, Ravi Kulan, & Birk, Andreas. (2013). A distributed algorithm for cooperative 3d exploration under communication constraints. Paladyn, Journal of Behavioral Robotics, 4(4), 223–232.

    Article  Google Scholar 

  35. Fox, Dieter, Ko, Jonathan, Konolige, Kurt, Limketkai, Benson, Schulz, Dirk, & Stewart, Benjamin. (2006). Distributed multirobot exploration and mapping. Proceedings of the IEEE, 94(7), 1325–1339.

    Article  MATH  Google Scholar 

  36. De Hoog, J., Cameron, S., & Visser, A.. (2009). Role-based autonomous multi-robot exploration. In Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATIONWORLD’09. Computation World:, (pp. 482–487). IEEE.

  37. Freda, L., & Oriolo, G. (2005). Frontier-based probabilistic strategies for sensor-based exploration. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, (pp. 3881–3887). IEEE.

  38. Burgard, W., Moors, M., Fox, D., Simmons, R., & Thrun, S. (2000). Collaborative multi-robot exploration. In Proceedings of the 2000 IEEE International Conference on Robotics and Automation, ICRA’00, (Vol. 1, pp. 476–481). IEEE.

  39. Yamauchi, B. (1998). Frontier-based exploration using multiple robots. In Proceedings of the Second International Conference on Autonomous Agents, (pp. 47–53). ACM.

  40. Kavraki, Lydia  E, Svestka, Petr, Latombe, J.-C., & Overmars, Mark  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 

  41. Kavraki, E. E., Kolountzakis, Mihail  N, & Latombe, J.-C. (1998). Analysis of probabilistic roadmaps for path planning. IEEE Transactions on Robotics and Automation, 14(1), 166–171.

    Article  Google Scholar 

  42. Prentice, S., & Roy, N. (2009). The belief roadmap: Efficient planning in belief space by factoring the covariance. The International Journal of Robotics Research.

  43. Kuffner, J. J., LaValle, S. M. (2000). Rrt-connect: An efficient approach to single-query path planning. In Proceedings of the IEEE International Conference on Robotics and Automation, 2000, Proceedings. ICRA’00, (Vol. 2, pp. 995–1001). IEEE.

  44. LaValle, Steven  Michael. (2006). Planning algorithms. Cambridge: Cambridge university press.

    Book  MATH  Google Scholar 

  45. Valencia, R., Andrade-Cetto, J., & Porta, J. M. (2011). Path planning in belief space with pose slam. In 2011 IEEE International Conference on Robotics and Automation (ICRA), (pp. 78–83). IEEE.

  46. Birk, Andreas, & Carpin, Stefano. (2006). Merging occupancy grid maps from multiple robots. Proceedings of the IEEE, 94(7), 1384–1397.

    Article  Google Scholar 

  47. Kollar, Thomas, & Roy, Nicholas. (2008). Trajectory optimization using reinforcement learning for map exploration. The International Journal of Robotics Research, 27(2), 175–196.

    Article  Google Scholar 

  48. Martinez-Cantin, Ruben, de Freitas, Nando, Brochu, Eric, Castellanos, José, & Doucet, Arnaud. (2009). A bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. Autonomous Robots, 27(2), 93–103.

    Article  Google Scholar 

  49. Peng, Wu, Suzuki, Hiromasa, & Kase, Kiwamu. (2005). Model-based simulation system for planning numerical controlled multi-axis 3d surface scanning machine. JSME International Journal Series C, 48, 748–756.

    Article  Google Scholar 

  50. Doucet, A. (1998). On sequential simulation-based methods for bayesian filtering. Technical report.

  51. Doitsidis, L., Weiss, S., Renzaglia, A., Achtelik, M. W., Kosmatopoulos, E. B., Siegwart, R., et al. (2012). Optimal surveillance coverage for teams of micro aerial vehicles in gps-denied environments using onboad vision. Autonomous Robots, 33(1–2), 173–188.

    Article  Google Scholar 

  52. Renzaglia, A., Doitsidis, L., Chatzichristofis, S. A., Martinelli, A., & Kosmatopoulos, E. B. (2013). Distributed multi-robot coverage using micro aerial vehicles. In 21st Mediterranean Conference on Control and Automation (pp. 963–968). MED13 Greece: Chania.

  53. Agile project’s website.

  54. Amanatiadis, A., Chatzichristofis, S. A., Charalampous, K., Doitsidis, L., Kosmatopoulos, E. B., Tsalides, P., et al. (2013). A multi-objective exploration strategy for mobile robots under operational constraints. IEEE Access, 1, 691–702.

    Article  Google Scholar 

  55. Ruppert, D., & Wand, M. P. (1994). Multivariate locally weighted least squares regression. The Annals of Statistics, (pp. 1346–1370).

  56. LSTS lab. Neptus Command and Control Software. http://lsts.fe.up.pt/software/neptus.

  57. LSTS lab. DUNE: Unified Navigation Environment. http://lsts.fe.up.pt/software/dune.

Download references

Acknowledgments

The research leading to these results has received funding from the European Communities Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 270180 (NOPTILUS).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Athanasios Ch. Kapoutsis.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kapoutsis, A.C., Chatzichristofis, S.A., Doitsidis, L. et al. Real-time adaptive multi-robot exploration with application to underwater map construction. Auton Robot 40, 987–1015 (2016). https://doi.org/10.1007/s10514-015-9510-8

Download citation

Keywords

  • Path planning for multiple mobile robot systems
  • Trajectory generation
  • Cognitive robotics
  • Mapping
  • Marine robotics