Autonomous Robots

, Volume 42, Issue 4, pp 715–738 | Cite as

Online planning for multi-robot active perception with self-organising maps

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Part of the following topical collections:
  1. Special Issue: Online Decision Making in Multi-Robot Coordination

Abstract

We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Exploration objectives for online tasks where the environment is only partially known in advance are modelled by introducing goal regions in unexplored space. Online replanning is performed efficiently by adapting previous solutions as new information becomes available. Simulations were performed using a 3D point-cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons.

Keywords

Active perception Multi-robot systems Self-organising maps Online planning 

References

  1. Angéniol, B., de la Vaubois, C. G., & Texier, J. Y. L. (1988). Self-organizing feature maps and the travelling salesman problem. Neural Networks, 1(4), 289–293.CrossRefGoogle Scholar
  2. Archetti, C., Hertz, A., & Speranza, M. G. (2007). Metaheuristics for the team orienteering problem. Journal of Heuristics, 13(1), 49–76.CrossRefGoogle Scholar
  3. Atanasov, N., Ny, J. L., Daniilidis, K., & Pappas, G. J. (2015). Decentralized active information acquisition: Theory and application to multi-robot SLAM. In Proceedings of IEEE ICRA (pp. 4775–4782).Google Scholar
  4. Atanasov, N., Sankaran, B., Le Ny, J., Pappas, G., & Daniilidis, K. (2014). Nonmyopic view planning for active object classification and pose estimation. IEEE Transactions on Robotics, 30(5), 1078–1090.CrossRefGoogle Scholar
  5. Bargoti, S., Underwood, J. P., Nieto, J. I., & Sukkarieh, S. (2015). A pipeline for trunk detection in trellis structured apple orchards. Journal of Field Robotics, 32(8), 1075–1094.CrossRefGoogle Scholar
  6. Becerra, I., Valentín-Coronado, L. M., Murrieta-Cid, R., & Latombe, J. C. (2016). Reliable confirmation of an object identity by a mobile robot: A mixed appearance/localization-driven motion approach. The International Journal of Robotics Research, 35(10), 1207–1233.CrossRefGoogle Scholar
  7. Bektas, T. (2006). The multiple traveling salesman problem: An overview of formulations and solution procedures. Omega, 34(3), 209–219.MathSciNetCrossRefGoogle Scholar
  8. Best, G., Cliff, O., Patten, T., Mettu, R., & Fitch, R. (2016a). Decentralised Monte Carlo tree search for active perception. In Proceedings of the WAFR.Google Scholar
  9. Best, G., Faigl, J., & Fitch, R. (2016b). Multi-robot path planning for budgeted active perception with self-organising maps. In Proceedings of IEEE/RSJ IROS (pp. 3164–3171).Google Scholar
  10. Best, G., & Fitch, R. (2016). Probabilistic maximum set cover with path constraints for informative path planning. In: Proceedings of ARAA ACRA.Google Scholar
  11. Best, G., Martens, W., & Fitch, R. (2017). Path planning with spatiotemporal optimal stopping for stochastic mission monitoring. IEEE Transactions on Robotics, 33(3), 629–646.CrossRefGoogle Scholar
  12. Binney, J., & Sukhatme, G. (2012). Branch and bound for informative path planning. In Proceedings of IEEE ICRA (pp. 2147–2154).Google Scholar
  13. Bircher, A., Kamel, M., Alexis, K., Burri, M., Oettershagen, P., Omari, S., et al. (2016). Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots. Autonomous Robots, 40(6), 1059–1078.CrossRefGoogle Scholar
  14. Bourgault, F., Makarenko, A., Williams, S., Grocholsky, B., & Durrant-Whyte, H. (2002). Information based adaptive robotic exploration. In Proceedings of IEEE/RSJ IROS (pp. 540–545).Google Scholar
  15. Cao, N., Low, K. H., & Dolan, J. M. (2013). Multi-robot informative path planning for active sensing of environmental phenomena: A tale of two algorithms. In Proceedings of AAMAS (pp. 7–14).Google Scholar
  16. Charrow, B. (2015). Information-theoretic active perception for multi-robot teams. Ph.D. thesis, University of Pennsylvania.Google Scholar
  17. Chekuri, C., & Pal, M. (2005). A recursive greedy algorithm for walks in directed graphs. In Proceedings of IEEE FOCS (pp. 245–253).Google Scholar
  18. Chen, S., Li, Y., & Kwok, N. M. (2011). Active vision in robotic systems: A survey of recent developments. The International Journal of Robotics Research, 30(11), 1343–1377.CrossRefGoogle Scholar
  19. Cochrane, E. M., & Beasley, J. E. (2003). The co-adaptive neural network approach to the euclidean travelling salesman problem. Neural Networks, 16(10), 1499–1525.CrossRefGoogle Scholar
  20. Corah, M., & Michael, N. (2017). Efficient online multi-robot exploration via distributed sequential greedy assignment. In Proceedings of robotics: science and systems.Google Scholar
  21. Cunningham-Nelson, S., Moghadam, P., Roberts, J., & Elfes, A. (2015). Coverage-based next best view selection. In Proceedings of ARAA ACRA.Google Scholar
  22. Dang, D. C., El-Hajj, R., & Moukrim, A. (2013a). A branch-and-cut algorithm for solving the team orienteering problem. In Proceedings of CPAIOR (pp. 332–339). Springer.Google Scholar
  23. Dang, D. C., Guibadj, R. N., & Moukrim, A. (2013b). An effective PSO-inspired algorithm for the team orienteering problem. European Journal of Operational Research, 229(2), 332–344.CrossRefMATHGoogle Scholar
  24. Dornhege, C., Kleiner, A., Hertle, A., & Kolling, A. (2016). Multirobot coverage search in three dimensions. Journal of Field Robotics, 33(4), 537–558.CrossRefGoogle Scholar
  25. Faigl, J. (2010). Approximate solution of the multiple watchman routes problem with restricted visibility range. IEEE Transactions on Neural Networks, 21(10), 1668–1679.CrossRefGoogle Scholar
  26. Faigl, J. (2016a). An application of self-organizing map for multirobot multigoal path planning with minmax objective. Computational Intelligence and Neuroscience.  https://doi.org/10.1155/2016/2720630.
  27. Faigl, J. (2016b). On self-organizing map and rapidly-exploring random graph in multi-goal planning. In Advances in self-organizing maps and learning vector quantization (pp. 143–153). Springer.Google Scholar
  28. Faigl, J. (2017). On self-organizing maps for orienteering problems. In Proceedings of IJCNN (pp. 2611–2620).Google Scholar
  29. Faigl, J., & Hollinger, G. (2014). Unifying multi-goal path planning for autonomous data collection. In Proceedings of IEEE/RSJ IROS (pp. 2937–2942).Google Scholar
  30. Faigl, J., & Hollinger, G. A. (2017). Autonomous data collection using a self-organizing map. IEEE Transactions on Neural Networks and Learning Systems.  https://doi.org/10.1109/TNNLS.2017.2678482.
  31. Faigl, J., Kulich, M., & Přeučil, L. (2012). Goal assignment using distance cost in multi-robot exploration. In Proceedings of IEEE/RSJ IROS (pp. 3741–3746).Google Scholar
  32. Faigl, J., Pěnička, R., & Best, G. (2016). Self-organizing map-based solution for the orienteering problem with neighborhoods. In Proceedings of IEEE SMC (pp. 1315–1321).Google Scholar
  33. Faigl, J., & Váňa, P. (2016). Self-organizing map for data collection planning in persistent monitoring with spatial correlations. In Proceedings of IEEE SMC (pp. 3264–3269).Google Scholar
  34. Galceran, E., & Carreras, M. (2013). A survey on coverage path planning for robotics. Robotics and Autonomous Systems, 61(12), 1258–1276.CrossRefGoogle Scholar
  35. Garg, S., & Ayanian, N. (2014). Persistent monitoring of stochastic spatio-temporal phenomena with a small team of robots. In Proceedings of robotics: science and systems.Google Scholar
  36. Gunawan, A., Lau, H. C., & Vansteenwegen, P. (2016). Orienteering problem: A survey of recent variants, solution approaches and applications. European Journal of Operational Research, 255(2), 315–332.MathSciNetCrossRefMATHGoogle Scholar
  37. Helsgaun, K. (2000). An effective implementation of the Lin–Kernighan traveling salesman heuristic. European Journal of Operational Research, 126(1), 106–130.MathSciNetCrossRefMATHGoogle Scholar
  38. Hollinger, G., Singh, S., Djugash, J., & Kehagias, A. (2009). Efficient multi-robot search for a moving target. The International Journal of Robotics Research, 28(2), 201–219.CrossRefGoogle Scholar
  39. Hollinger, G. A., Mitra, U., & Sukhatme, G. S. (2011). Active classification: Theory and application to underwater inspection. In Proceedings of ISRR.Google Scholar
  40. Hönig, W., & Ayanian, N. (2016). Dynamic multi-target coverage with robotic cameras. In Proceedings of IEEE/RSJ IROS (pp. 1871–1878).Google Scholar
  41. Kassir, A., Fitch, R., & Sukkarieh, S. (2015). Communication-aware information gathering with dynamic information flow. The International Journal of Robotics Research, 34(2), 173–200.CrossRefGoogle Scholar
  42. Kriegel, S., Brucker, M., Marton, Z. C., Bodenmuller, T., & Suppa, M. (2013). Combining object modeling and recognition for active scene exploration. In Proceedings of IEEE/RSJ IROS (pp. 2384–2391).Google Scholar
  43. Kulich, M., Faigl, J., & Přeučil, L. (2011). On distance utility in the exploration task. In Proceedings of IEEE ICRA (pp. 4455–4460).Google Scholar
  44. Kulich, M., Sushkov, R., & Přeučil, L. (2016). Speed-up of self-organizing networks for routing problems in a polygonal domain. In Proceedings of IEEE/RSJ IROS 10th international workshop on cognitive robotics.Google Scholar
  45. Lagoudakis, M. G., Markakis, E., Kempe, D., Keskinocak, P., Kleywegt, A. J., Koenig, S., Tovey, C. A., Meyerson, A., & Jain, S. (2005). Auction-based multi-robot routing. In Proceedings of robotics: science and systems.Google Scholar
  46. Likhachev, M., Ferguson, D. I., Gordon, G. J., Stentz, A., & Thrun, S. (2005). Anytime dynamic A*: An anytime, replanning algorithm. In Proceedings of ICAPS (pp. 262–271).Google Scholar
  47. Martens, W., Poffet, Y., Soria, P. R., Fitch, R., & Sukkarieh, S. (2017). Geometric priors for Gaussian process implicit surfaces. IEEE Robotics and Automation Letters, 2(2), 373–380.CrossRefGoogle Scholar
  48. Mathew, N., Smith, S., & Waslander, S. (2013). A graph-based approach to multi-robot rendezvous for recharging in persistent tasks. In Proceedings of IEEE ICRA (pp. 3497–3502).Google Scholar
  49. McMahon, J., & Plaku, E. (2017). Autonomous data collection with limited time for underwater vehicles. IEEE Robotics and Automation Letters, 2(1), 112–119.CrossRefGoogle Scholar
  50. Noon, C. E., & Bean, J. C. (1989). An efficient transformation of the generalized traveling salesman problem. Technical report 89-36, Department of Industrial and Operations Engineering, University of Michigan.Google Scholar
  51. Patten, T. (2017). Active object classification from 3D range data with mobile robots. Ph.D. thesis, The University of Sydney.Google Scholar
  52. Patten, T., Kassir, A., Martens, W., Douillard, B., Fitch, R., & Sukkarieh, S. (2015). A Bayesian approach for time-constrained 3D outdoor object recognition. In: Proceedings of IEEE ICRA workshop on scaling up active perception.Google Scholar
  53. Patten, T., Martens, W., & Fitch, R. (2017). Monte Carlo planning for active object classification. Autonomous Robots.  https://doi.org/10.1007/s10514-017-9626-0.
  54. Patten, T., Zillich, M., Fitch, R., Vincze, M., & Sukkarieh, S. (2016). Viewpoint evaluation for online 3-D active object classification. IEEE Robotics and Automation Letters, 1(1), 73–81.CrossRefGoogle Scholar
  55. Peng, C., Roy, P., Luby, J., & Isler, V. (2016). Semantic mapping of orchards. IFAC-PapersOnLine, 49(16), 85–89.CrossRefGoogle Scholar
  56. Quattrini Li, A., Cipolleschi, R., Giusto, M., & Amigoni, F. (2016). A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings. Autonomous Robots, 40(4), 581–597.CrossRefGoogle Scholar
  57. Robin, C., & Lacroix, S. (2015). Multi-robot target detection and tracking: Taxonomy and survey. Autonomous Robots, 40(4), 729–760.CrossRefGoogle Scholar
  58. Singh, A., Krause, A., Guestrin, C., & Kaiser, W. J. (2009). Efficient informative sensing using multiple robots. Journal of Artificial Intelligence Research, 34(2), 707.MathSciNetMATHGoogle Scholar
  59. Smith, S. L., & Imeson, F. (2017). GLNS: An effective large neighborhood search heuristic for the generalized traveling salesman problem. Computers and Operations Research, 87, 1–19.MathSciNetCrossRefGoogle Scholar
  60. Somhom, S., Modares, A., & Enkawa, T. (1997). A self-organising model for the travelling salesman problem. Journal of the Operational Research Society, 48(9), 919–928.CrossRefMATHGoogle Scholar
  61. Somhom, S., Modares, A., & Enkawa, T. (1999). Competition-based neural network for the multiple travelling salesmen problem with minmax objective. Computers and Operations Research, 26(4), 395–407.MathSciNetCrossRefMATHGoogle Scholar
  62. Toth, P., & Vigo, D. (2001). The vehicle routing problem. New Delhi: SIAM.MATHGoogle Scholar
  63. Tucci, M., & Raugi, M. (2010). Stability analysis of self-organizing maps and vector quantization algorithms. In Proceedings of IJCNN (pp. 1–5).Google Scholar
  64. van Hoof, H., Kroemer, O., & Peters, J. (2014). Probabilistic segmentation and targeted exploration of objects in cluttered environments. IEEE Transactions on Robotics, 30(5), 1198–1209.CrossRefGoogle Scholar
  65. Vansteenwegen, P., Souffriau, W., & Oudheusden, D. V. (2011). The orienteering problem: A survey. European Journal of Operational Research, 209(1), 1–10.MathSciNetCrossRefMATHGoogle Scholar
  66. Wohlkinger, W., Aldoma, A., Rusu, R. B., & Vincze, M. (2012). 3DNet: Large-scale object class recognition from CAD models. In Proceedings of IEEE ICRA (pp. 5384–5391).Google Scholar
  67. Wohlkinger, W., & Vincze, M. (2011). Ensemble of shape functions for 3D object classification. In Proceedings of IEEE ROBIO (pp. 2987–2992).Google Scholar
  68. Wu, K., Ranasigne, R., & Dissanayake, G. (2015). Active recognition and pose estimation of household objects in clutter. In Proceedings of IEEE ICRA (pp. 4230–4237).Google Scholar
  69. Xu, Z., Fitch, R., Underwood, J., & Sukkarieh, S. (2013). Decentralized coordinated tracking with mixed discrete-continuous decisions. Journal of Field Robotics, 30(5), 717–740.CrossRefGoogle Scholar
  70. Yu, J., Schwager, M., & Rus, D. (2016). Correlated orienteering problem and its application to persistent monitoring tasks. IEEE Transactions on Robotics, 32(5), 1106–1118.CrossRefGoogle Scholar
  71. Zhang, W. D., Bai, Y. P., & Hu, H. P. (2006). The incorporation of an efficient initialization method and parameter adaptation using self-organizing maps to solve the TSP. Applied Mathematics and Computation, 172(1), 603–623.MathSciNetCrossRefMATHGoogle Scholar
  72. Zlot, R., Stentz, A., Dias, M., & Thayer, S. (2002). Multi-robot exploration controlled by a market economy. In Proceedings of IEEE ICRA (Vol. 3, pp. 3016–3023).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Australian Centre for Field Robotics (ACFR)The University of SydneyCamperdownAustralia
  2. 2.Department of Computer Science, Faculty of Electrical Engineering (FEE)Czech Technical University in PraguePragueCzech Republic
  3. 3.Centre for Autonomous SystemsUniversity of Technology SydneyUltimoAustralia

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