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

- 761 Downloads
- 1 Citations

**Part of the following topical collections:**

## 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

- 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 - Archetti, C., Hertz, A., & Speranza, M. G. (2007). Metaheuristics for the team orienteering problem.
*Journal of Heuristics*,*13*(1), 49–76.CrossRefGoogle Scholar - 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 - 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 - 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 - 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 - Bektas, T. (2006). The multiple traveling salesman problem: An overview of formulations and solution procedures.
*Omega*,*34*(3), 209–219.MathSciNetCrossRefGoogle Scholar - 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 - 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 - Best, G., & Fitch, R. (2016). Probabilistic maximum set cover with path constraints for informative path planning. In:
*Proceedings of ARAA ACRA*.Google Scholar - 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 - Binney, J., & Sukhatme, G. (2012). Branch and bound for informative path planning. In
*Proceedings of IEEE ICRA*(pp. 2147–2154).Google Scholar - 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 - 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 - 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 - Charrow, B. (2015).
*Information-theoretic active perception for multi-robot teams*. Ph.D. thesis, University of Pennsylvania.Google Scholar - Chekuri, C., & Pal, M. (2005). A recursive greedy algorithm for walks in directed graphs. In
*Proceedings of IEEE FOCS*(pp. 245–253).Google Scholar - 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 - 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 - Corah, M., & Michael, N. (2017). Efficient online multi-robot exploration via distributed sequential greedy assignment. In
*Proceedings of robotics: science and systems*.Google Scholar - Cunningham-Nelson, S., Moghadam, P., Roberts, J., & Elfes, A. (2015). Coverage-based next best view selection. In
*Proceedings of ARAA ACRA*.Google Scholar - 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 - 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.CrossRefzbMATHGoogle Scholar - 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 - 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 - 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. - 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 - Faigl, J. (2017). On self-organizing maps for orienteering problems. In
*Proceedings of IJCNN*(pp. 2611–2620).Google Scholar - 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 - 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. - 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 - 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 - 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 - Galceran, E., & Carreras, M. (2013). A survey on coverage path planning for robotics.
*Robotics and Autonomous Systems*,*61*(12), 1258–1276.CrossRefGoogle Scholar - 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 - 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.MathSciNetCrossRefzbMATHGoogle Scholar - Helsgaun, K. (2000). An effective implementation of the Lin–Kernighan traveling salesman heuristic.
*European Journal of Operational Research*,*126*(1), 106–130.MathSciNetCrossRefzbMATHGoogle Scholar - 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 - Hollinger, G. A., Mitra, U., & Sukhatme, G. S. (2011). Active classification: Theory and application to underwater inspection. In
*Proceedings of ISRR*.Google Scholar - Hönig, W., & Ayanian, N. (2016). Dynamic multi-target coverage with robotic cameras. In
*Proceedings of IEEE/RSJ IROS*(pp. 1871–1878).Google Scholar - 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 - 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 - Kulich, M., Faigl, J., & Přeučil, L. (2011). On distance utility in the exploration task. In
*Proceedings of IEEE ICRA*(pp. 4455–4460).Google Scholar - 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 - 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 - 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 - 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 - 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 - 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 - 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
- Patten, T. (2017).
*Active object classification from 3D range data with mobile robots*. Ph.D. thesis, The University of Sydney.Google Scholar - 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 - 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. - 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 - Peng, C., Roy, P., Luby, J., & Isler, V. (2016). Semantic mapping of orchards.
*IFAC-PapersOnLine*,*49*(16), 85–89.CrossRefGoogle Scholar - 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 - Robin, C., & Lacroix, S. (2015). Multi-robot target detection and tracking: Taxonomy and survey.
*Autonomous Robots*,*40*(4), 729–760.CrossRefGoogle Scholar - Singh, A., Krause, A., Guestrin, C., & Kaiser, W. J. (2009). Efficient informative sensing using multiple robots.
*Journal of Artificial Intelligence Research*,*34*(2), 707.MathSciNetzbMATHGoogle Scholar - 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 - 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.CrossRefzbMATHGoogle Scholar - 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.MathSciNetCrossRefzbMATHGoogle Scholar - Toth, P., & Vigo, D. (2001).
*The vehicle routing problem*. New Delhi: SIAM.zbMATHGoogle Scholar - Tucci, M., & Raugi, M. (2010). Stability analysis of self-organizing maps and vector quantization algorithms. In
*Proceedings of IJCNN*(pp. 1–5).Google Scholar - 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 - Vansteenwegen, P., Souffriau, W., & Oudheusden, D. V. (2011). The orienteering problem: A survey.
*European Journal of Operational Research*,*209*(1), 1–10.MathSciNetCrossRefzbMATHGoogle Scholar - 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 - Wohlkinger, W., & Vincze, M. (2011). Ensemble of shape functions for 3D object classification. In
*Proceedings of IEEE ROBIO*(pp. 2987–2992).Google Scholar - 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 - 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 - 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 - 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.MathSciNetCrossRefzbMATHGoogle Scholar - 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