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Global Localization of Unmanned Ground Vehicles Using Swarm Intelligence and Evolutionary Algorithms

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Abstract

Mobile robot localization is a complex task, specially in unstructured indoor environments, due to noise and wrong scan-to-map association. The localization procedure becomes critical when the vehicle has low confidence about its last pose estimate, situation that requires a global localization procedure. An intuitive approach to solve the Global Localization Problem (GLP) is to distribute several pose hypotheses all over the map and select the most likely one according to an optimization heuristic such as Monte Carlo, Swarm Intelligence or Evolutionary Algorithm. However, hardware limitations and environment characteristics may affect the localization efficacy. Furthermore, we found relatively few studies exploring the effectiveness and the computing cost of different localization methods under different scenarios e.g. offices, corridors and big warehouses. In this work, we analyze different global localization methods based on multi-hypothesis optimization metaheuristics. We use the scan-to-map matching error computed by a pose tracking algorithm, the Perfect Match (PM), as the metric to score the hypotheses. Our main contribution is to propose an enhanced localization system by integrating a multi-hypothesis global localization method with the PM. We also analyzed different optimization heuristics applied to the GLP under typical and special conditions. Using simulations and real-world experiments, we measured the success rate and computing cost using several population sizes. Results show that studied methods perform differently in distinct scenarios, but our proposals based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) showed an average success rate above 83%, while other methods did not achieved 80%. Furthermore, PM-based methods exhibit lower computing cost when compared to the traditional Adaptive Monte Carlo Localization (AMCL) after the 100th iteration. In summary, our study shows that the GA-based proposal, which performed slightly better than the PSO-based, represents the best candidate to integrate a robust localization system.

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Code and Data Availability

The source code developed by the authors for this study and the datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request. The Perfect Match software, whose source code was used under license for the current study, is a third party property and so is not publicly available. The Intel Research Lab dataset is a public domain resource under license “Creative Commons CC0 1.0 Universal” and was obtained from the digital repository for MIT’s research DSpace@MIT, available at https://dspace.mit.edu/handle/1721.1/62287.

References

  1. Corke, P.: Robotics, Vision and Control: Fundamental Algorithms in MATLAB® Second, completely revised, vol. 118. Springer, Cham (2017). https://doi.org/10.1007/978-3-642-20144-8

    Book  Google Scholar 

  2. Joubert, D., Brink, W., Herbst, B.: Pose uncertainty in occupancy grids through monte carlo integration. J. Intell. Robot. Syst. 77(1), 5–16 (2015). https://doi.org/10.1007/s10846-014-0093-y

    Article  Google Scholar 

  3. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)

    MATH  Google Scholar 

  4. Almeida, T., Santos, V., Mozos, O.M., Lourenço, B.: Comparative analysis of deep neural networks for the detection and decoding of data matrix landmarks in cluttered indoor environments. J. Intell. Robot. Syst. 103(1), 1–14 (2021). https://doi.org/10.1007/s10846-021-01442-x

    Article  Google Scholar 

  5. Digiacomo, F., Bologna, F., Inglese, F., Stefanini, C., Milazzo, M.: MechaTag: A mechanical fiducial marker and the detection algorithm. J. Intell. Robot. Syst. 103(3), 1–11 (2021). https://doi.org/10.1007/s10846-021-01507-x

    Article  Google Scholar 

  6. Pei, F., Zhu, M., Wu, X.: A decorrelated distributed EKF-SLAM system for the autonomous navigation of mobile robots. J. Intell. Robot. Syst. 98(3), 819–829 (2020). https://doi.org/10.1007/s10846-019-01069-z

    Article  Google Scholar 

  7. Prakash, K., Mohamed, M.N.G., Chakravorty, S., Hasnain, Z.: Structure aided Odometry (SAO): A novel analytical Odometry technique based on semi-absolute localization for precision-warehouse robotic assistance in environments with low feature variation. J. Intell. Robot. Syst. 102(4), 1–24 (2021). https://doi.org/10.1007/s10846-021-01427-w

    Article  Google Scholar 

  8. Sobreira, H., Rocha, L., Costa, C., Lima, J., Costa, P., Moreira, A.P.: 2D cloud template matching - a comparison between iterative closest point and perfect match. In: 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp 53–59 (2016). https://doi.org/10.1109/ICARSC.2016.13

  9. Filotheou, A., Tsardoulias, E., Dimitriou, A., Symeonidis, A., Petrou, L.: Pose selection and feedback methods in tandem combinations of particle filters with scan-matching for 2D mobile robot localisation. J. Intell. Robot. Syst. 100(3), 925–944 (2020). https://doi.org/10.1007/s10846-020-01253-6

    Article  Google Scholar 

  10. Lauer, M., Lange, S., Riedmiller, M.: Calculating the perfect match: an efficient and accurate approach for robot self-localization. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) Robot Soccer World Cup, pp 142–153. Springer, Berlin (2005). https://doi.org/10.1007/11780519_13

  11. Sobreira, H., Costa, C.M., Sousa, I., Rocha, L., Lima, J., Farias, P., Costa, P., Moreira, A.P.: Map-matching algorithms for robot self-localization: a comparison between perfect match, iterative closest point and normal distributions transform. J. Intell. Robot. Syst. 93(3), 533–546 (2019). https://doi.org/10.1007/s10846-017-0765-5

    Article  Google Scholar 

  12. Zhang, L., Zapata, R., Lépinay, P.: Self-adaptive monte carlo localization for mobile robots using range sensors. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 1541–1546 (2009), https://doi.org/10.1109/IROS.2009.5354298

  13. Pinto, M., Sobreira, H., Moreira, A.P., Mendonca̧, H., Matos, A.: Self-localisation of indoor mobile robots using multi-hypotheses and a matching algorithm. Mechatronics 23(6), 727–737 (2013). https://doi.org/10.1016/j.mechatronics.2013.07.006

    Article  Google Scholar 

  14. Campbell, S., O’Mahony, N., Carvalho, A., Krpalkova, L., Riordan, D., Walsh, J.: Where am I? localization techniques for mobile robots a review. In: 2020 6th International Conference on Mechatronics and Robotics Engineering (ICMRE), pp 43–47 (2020). https://doi.org/10.1109/ICMRE49073.2020.9065135

  15. Wu, Z., Zhang, J., Yue, Y., Wen, M., Jiang, Z., Zhang, H., Wang, D.: Infrastructure-free global localization in repetitive environments: An overview. In: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, pp 626–631 (2020). https://doi.org/10.1109/IECON43393.2020.9255046

  16. Ivanjko, E., Petrovic, I.: Extended Kalman filter based mobile robot pose tracking using occupancy grid maps. In: Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521), vol. 1, pp 311–3141 (2004). https://doi.org/10.1109/MELCON.2004.1346851

  17. Minguez, J., Montesano, L., Lamiraux, F.: Metric-based iterative closest point scan matching for sensor displacement estimation. IEEE Trans. Robot. 22(5), 1047–1054 (2006). https://doi.org/10.1109/TRO.2006.878961

    Article  Google Scholar 

  18. Konecny, J., Prauzek, M., Hlavica, J.: ICP algorithm in mobile robot navigation: Analysis of computational demands in embedded solutions. IFAC-PapersOnLine 49(25), 396–400 (2016). https://doi.org/10.1016/j.ifacol.2016.12.079

    Article  Google Scholar 

  19. Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. Int. J. Robot. Res. 21(8), 735–758 (2002). https://doi.org/10.1177/027836402761412467

    Article  Google Scholar 

  20. Choi, J., Maurer, M.: Hybrid map-based SLAM with Rao-Blackwellized particle filters. In: 17th International Conference on Information Fusion (FUSION), pp 1–6, Salamanca, Spain (2014)

  21. Lee, H., Chun, J., Jeon, K., Lee, H.: Efficient EKF-SLAM algorithm based on measurement clustering and real data simulations. In: 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), pp 1–5 (2018). https://doi.org/10.1109/VTCFall.2018.8690802

  22. Joo, S.-H., Lee, U.-H., Kuc, T.-Y., Park, J.-K.: A robust SLAM algorithm using hybrid map approach. In: 2018 International Conference on Electronics, Information, and Communication (ICEIC), pp 1–2 (2018). https://doi.org/10.23919/ELINFOCOM.2018.8330614

  23. Bouraine, S., Bougouffa, A., Azouaoui, O.: Particle swarm optimization for solving a scan-matching problem based on the normal distributions transform. Evol. Intel., 1–12. https://doi.org/10.1007/s12065-020-00545-y (2021)

  24. Chien, C.-H., Wang, W.-Y., Jo, J., Hsu, C.-C.: Enhanced monte carlo localization incorporating a mechanism for preventing premature convergence. Robotica 35(7), 1504 (2017). https://doi.org/10.1017/S026357471600028X

    Article  Google Scholar 

  25. Yilmaz, A., Temeltas, H.: Self-adaptive monte carlo method for indoor localization of smart AGVs using LIDAR data. Robot. Auton. Syst. 122, 103285 (2019). https://doi.org/10.1016/j.robot.2019.103285

    Article  Google Scholar 

  26. AbuAlkebash, H., Hasan, O.: Improved global localization and resampling techniques for Monte Carlo localization algorithm. Int. J. Appl. Math. Electron. Comput. 8(3), 102–108 (2020). https://doi.org/10.18100/ijamec.800166

    Article  Google Scholar 

  27. Chien, C.-H., Hsu, C.-C., Wang, W.-Y., Kao, W.-C., Chien, C.-J.: Global localization of Monte Carlo localization based on multi-objective particle swarm optimization. In: 2016 IEEE 6th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), pp 96–97 (2016). https://doi.org/10.1109/ICCE-Berlin.2016.7684728

  28. Chien, C.-H., Wang, W.-Y., Hsu, C.-C.: Multi-objective evolutionary approach to prevent premature convergence in Monte Carlo localization. Appl. Soft Comput. 50, 260–279 (2017). https://doi.org/10.1016/j.asoc.2016.11.020

    Article  Google Scholar 

  29. Peng, G., Zheng, W., Lu, Z., Liao, J., Hu, L., Zhang, G., He, D.: An improved AMCL algorithm based on laser scanning match in a complex and unstructured environment. Complexity 2018. https://doi.org/10.1155/2018/2327637 (2018)

  30. Martín, F., Moreno, L., Garrido, S., Blanco, D.: Kullback-Leibler divergence-based differential evolution Markov Chain filter for global localization of mobile robots. Sensors 15(9), 23431–23458 (2015). https://doi.org/10.3390/s150923431

    Article  Google Scholar 

  31. Moreno, L., Martín, F., Muñoz, M.L., Garrido, S.: Differential evolution Markov Chain filter for global localization. J. Intell. Robot. Syst. 82(3-4), 513–536 (2016). https://doi.org/10.1007/s10846-015-0245-8

    Article  Google Scholar 

  32. Zhang, Q., Wang, P., Bao, P., Chen, Z.: Mobile robot global localization using particle swarm optimization with a 2D range scan. In: Proceedings of the 2017 International Conference on Robotics and Artificial Intelligence, pp 105–109 (2017). https://doi.org/10.1145/3175603.3175618

  33. Zhang, Q.-B., Wang, P., Chen, Z.-H.: An improved particle filter for mobile robot localization based on particle swarm optimization. Expert Syst. Appl. 135, 181–193 (2019). https://doi.org/10.1016/j.eswa.2019.06.006

    Article  Google Scholar 

  34. Pinto, A.M., Moreira, A.P., Costa, P.G.: A localization method based on map-matching and particle swarm optimization. J. Intell. Robot. Syst. 77(2), 313–326 (2015). https://doi.org/10.1007/s10846-013-0009-2

    Article  Google Scholar 

  35. Vahdat, A.R., NourAshrafoddin, N., Ghidary, S.S.: Mobile robot global localization using differential evolution and particle swarm optimization. In: 2007 IEEE Congress on Evolutionary Computation, pp 1527–1534 (2007), https://doi.org/10.1109/CEC.2007.4424654

  36. Neto, W.A., Pinto, M.F, Marcato, A.L., da Silva, I.C., Fernandes, D.D.A.: Mobile robot localization based on the novel leader-based bat algorithm. J. Control Autom. Electr. Syst. 30(3), 337–346 (2019). https://doi.org/10.1007/s40313-019-00453-2

    Article  Google Scholar 

  37. Nedjah, N., de Oliveira, P.J.A., et al.: Simultaneous localization and mapping using swarm intelligence based methods. Expert Syst. Appl. 159, 113547 (2020). https://doi.org/10.1016/j.eswa.2020.113547

    Article  Google Scholar 

  38. Holland, J.H.: Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Book  Google Scholar 

  39. Gao, H., Zhang, X., Yuan, J., Song, J., Fang, Y.: A novel global localization approach based on structural unit encoding and multiple hypothesis tracking. IEEE Trans. Instrum. Meas. 68(11), 4427–4442 (2019). https://doi.org/10.1109/TIM.2018.2890455

    Article  Google Scholar 

  40. Carvalho, J.L.C., Farias, P.C.M.A., de Souza, E.E.P., de Simas Filho, E.F.: Particle swarm localization for mobile robots using a 2D laser sensor. In: 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), pp 281–286 (2019). https://doi.org/10.1109/BRACIS.2019.00057

  41. Pinto, M., Moreira, A.P., Matos, A., Sobreira, H.: Novel 3D matching self-localisation algorithm. Int. J. Adv. Eng. Technol 5(1), 1–12 (2012)

    Google Scholar 

  42. Sobreira, H., Pinto, M., Moreira, A.P., Costa, P.G., Lima, J.: Robust robot localization based on the perfect match algorithm. In: CONTROLO’2014–Proceedings of the 11th Portuguese Conference on Automatic Control, pp 607–616 (2015). https://doi.org/10.1007/978-3-319-10380-8_58

  43. Farias, P., Sousa, I., Sobreira, H., Moreira, A.P.: Approach for supervising self-localization processes in mobile robots. In: EPIA Conference on Artificial Intelligence, pp 461–472. Springer (2017). https://doi.org/10.1007/978-3-319-65340-2_38

  44. Shafii, N., Farias, P., Sousa, I., Sobreira, H., Reis, L.P., Moreira, A.P.: Autonomous interactive object manipulation and navigation capabilities for an intelligent wheelchair. In: EPIA Conference on Artificial Intelligence, pp 473–485. Springer (2017). https://doi.org/10.1007/978-3-319-65340-2_39

  45. Fox, D., Burgard, W., Thrun, S.: Active Markov localization for mobile robots. Robot. Auton. Syst. 25(3-4), 195–207 (1998). https://doi.org/10.1016/S0921-8890(98)00049-9

    Article  MATH  Google Scholar 

  46. Seifzadeh, S., Wu, D., Wang, Y.: Cost-effective active localization technique for mobile robots. In: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp 539–543 (2009). https://doi.org/10.1109/ROBIO.2009.5420704

  47. Gottipati, S.K., Seo, K., Bhatt, D., Mai, V., Murthy, K., Paull, L.: Deep active localization. IEEE Robot. Autom. Lett. 4(4), 4394–4401 (2019). https://doi.org/10.1109/LRA.2019.2932575

    Article  Google Scholar 

  48. Andrade, F., LLofriu, M., Tanco, M.M., Barnech, G.T., Tejera, G.: Active localization for mobile service robots in symmetrical and open environments. In: 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE), pp 270–275 (2021). https://doi.org/10.1109/LARS/SBR/WRE54079.2021.9605406

  49. Fox, D.: KLD-sampling: Adaptive particle filters and mobile robot localization. Adv. Neural Inform. Process. Syst. (NIPS) 14(1), 26–32 (2001)

    Google Scholar 

  50. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp 1942–19484 (1995), https://doi.org/10.1109/ICNN.1995.488968

  51. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), vol. 1, pp 84–881 (2000), https://doi.org/10.1109/CEC.2000.870279

  52. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997). https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  53. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: a Practical Approach to Global Optimization. Springer, Berlin (2006). https://doi.org/10.1007/3-540-31306-0

    MATH  Google Scholar 

  54. Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007). https://doi.org/10.1109/TRO.2006.889486

    Article  Google Scholar 

  55. Kohlbrecher, S., von Stryk, O., Meyer, J., Klingauf, U.: A flexible and scalable slam system with full 3D motion estimation. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, pp 155–160 (2011). https://doi.org/10.1109/SSRR.2011.6106777

  56. Howard, A., Roy, N.: The Robotics Data Set Repository (Radish). http://radish.sourceforge.net/ (2003)

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Funding

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement N.777096 and from SEPIN/MCTI under the 4th Coordinated Call BR-EU in CIT.

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Authors and Affiliations

  1. Digital Systems Laboratory, Federal University of Bahia, Salvador, Bahia, Brazil

    João L. C. Carvalho, Paulo C. M. A. Farias & Eduardo Furtado Simas Filho

  2. Center for Science and Technology in Energy and Sustainability, Federal University of Recôncavo da Bahia, Feira de Santana, Bahia, Brazil

    João L. C. Carvalho

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  1. João L. C. Carvalho
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Contributions

All authors (Carvalho, J.L.C., Farias, P.C.M.A. and Simas Filho, E.F.) contributed equally to the study conception, design of the experiments, data analysis and the paper writing/review. Carvalho, J.L.C. and Farias, P.C.M.A. were responsible for the experiments planning, setup and generation of the dataset. Carvalho, J.L.C. developed the source code of the proposed methods, the scripts for data analysis and conducted the execution of the experiments.

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Correspondence to João L. C. Carvalho.

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Carvalho, J.L.C., Farias, P.C.M.A. & Simas Filho, E.F. Global Localization of Unmanned Ground Vehicles Using Swarm Intelligence and Evolutionary Algorithms. J Intell Robot Syst 107, 45 (2023). https://doi.org/10.1007/s10846-023-01813-6

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