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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

  • Received : 2018.10.05
  • Accepted : 2019.03.12
  • Published : 2019.12.06
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Abstract

This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large-scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi-Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.

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References

  1. P. Nazemzadeh et al., Indoor localization of mobile robots through QR code detection and dead reckoning data fusion, IEEE/ASME Trans. Mechatronics 22 (2017), 2588-2599. https://doi.org/10.1109/TMECH.2017.2762598
  2. M. Cap et al., Prioritized planning algorithms for trajectory coordination of multiple mobile robots, IEEE Trans. Autom. Sci. Eng. 12 (2015), 835-849. https://doi.org/10.1109/TASE.2015.2445780
  3. I. Nielsen et al., A methodology for implementation of mobile robot in adaptive manufacturing environments, J. Intell. Manuf. 28 (2017), 1171-1188. https://doi.org/10.1007/s10845-015-1072-2
  4. R. Triebel et al., Spencer: A socially aware service robot for passenger guidance and help in busy airports, in Proc, Int. Conf. Field Service Robotics, 2016, pp. 607-622.
  5. S. Azenkot, C. Feng, and M. Cakmak, Enabling building service robots to guide blind people a participatory design approach, in Proc. ACM/IEEE Int. Conf. Human-Robot Interaction, Christchurch, New Zealand, 2016, pp. 3-10.
  6. A. Pennisi et al., Multi-robot surveillance through a distributed sensor network, in Proc. Cooperative Robots and Sensor Networks, 2015, pp. 77-98.
  7. F. Donadio et al., Artificial intelligence and collaborative robot to improve airport operations, in Proc. Int. Conf. Online Eng. Internet Things, New York, USA, Mar. 2018, pp. 973-986.
  8. G. Lozenguez et al., Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation, Auton. Rob. 40 (2016), 599-613. https://doi.org/10.1007/s10514-015-9483-7
  9. K. Hausman et al., Cooperative multi-robot control for target tracking with onboard sensing, Int. J. Rob. Res. 34 (2015), 1660-1677. https://doi.org/10.1177/0278364915602321
  10. H. Yu et al., Human-robot interaction control of rehabilitation robots with series elastic actuators, IEEE Trans. Robot. 31 (2015), 1089-1100. https://doi.org/10.1109/TRO.2015.2457314
  11. B. Kehoe et al., A survey of research on cloud robotics and automation, IEEE Trans. Autom. Sci. Eng. 12 (2015), 398-409. https://doi.org/10.1109/TASE.2014.2376492
  12. J. A. Stankovic, When sensor and actuator networks cover the world, ETRI J. 30 (2008), 627-633. https://doi.org/10.4218/etrij.08.1308.0099
  13. R. Kummerle et al., Autonomous robot navigation in highly populated pedestrian zones, J. Field Robot. 32 (2015), 565-589. https://doi.org/10.1002/rob.21534
  14. H. Kretzschmar et al., Socially compliant mobile robot navigation via inverse reinforcement learning, Int. J. Rob. Res. 35 (2016), 1289-1307. https://doi.org/10.1177/0278364915619772
  15. S. Gil et al., Adaptive communication in multi-robot systems using directionality of signal strength, Int. J. Rob. Res. 34 (2015), 946-968. https://doi.org/10.1177/0278364914567793
  16. K. H. Choi, Y.-S. Jeong, and J.-M. Gil, Design and implementation of P2P home monitoring system architecture with IP cameras for a vacuum robot in ubiquitous environments, Int. J. Sens. N. 22 (2016), 166-176. https://doi.org/10.1504/IJSNET.2016.080200
  17. P. Simoens, M. Dragone, and A. Saffiotti, The internet of robotic things: A review of concept, added value and applications, Int. J. Adv. Robot. Syst. 15 (2018), no. 1, 1-11.
  18. S. Thrun, W. Burgard, and D. Fox, In Probabilistic Robotics, MIT press, 2005.
  19. Z. Jiang et al., A new kind of accurate calibration method for robotic kinematic parameters based on the extended Kalman and particle filter algorithm, IEEE Trans. on Ind. Electron. 65 (2018), 3337-3345. https://doi.org/10.1109/TIE.2017.2748058
  20. H. Myung et al., Mobile robot localization with gyroscope and constrained Kalman filter, Int. J. Control Autom. Syst. 8 (2010), 667-676. https://doi.org/10.1007/s12555-010-0321-6
  21. M. A. Mahmud et al, Kalman filter based indoor mobile robot navigation, in Proc. Int. Conf. Electr., Electron. Optimization Techn., Chennai, India, Mar. 2016, pp. 1949-1953.
  22. Z. Javad, Y. Cai, and Y. Majid, Comparing EKF and SPKF algorithms for simultaneous localization and mapping, J. Robot. N. Artif. Life 3 (2017), 217-220. https://doi.org/10.2991/jrnal.2017.3.4.2
  23. N. Ayadi et al., Simulation and experimental evaluation of the EKF simultaneous localization and mapping algorithm on the Wifibot mobile robot, J. Artif. Intell. Soft Com. Res. 8 (2018), 91-101. https://doi.org/10.1515/jaiscr-2018-0006
  24. M. Cui et al., An adaptive unscented Kalman filter-based controller for simultaneous obstacle avoidance and tracking of wheeled mobile robots with unknown slipping parameters, J. Intell. Robot. Syst. 92 (2018), 489-504. https://doi.org/10.1007/s10846-017-0761-9
  25. E. Colle and S. Galerne, A multihypothesis set approach for mobile robot localization using heterogeneous measurements provided by the internet of things, Robot. Aut. Syst. 96 (2017), 102-113. https://doi.org/10.1016/j.robot.2017.05.016
  26. D. Liu, J. Duan, and H. Shi, A strong tracking square root central difference FastSLAM for unmanned intelligent vehicle with adaptive partial systematic resampling, IEEE Trans. Intell. Transp. Syst. 17 (2016), 3110-3120. https://doi.org/10.1109/TITS.2016.2542098
  27. S. Saeedi et al., Multiple-robot simultaneous localization and mapping: A review, J. Field Robot. 33 (2016), 3-46. https://doi.org/10.1002/rob.21620
  28. D. Fox, W. Burgard, and S. Thrun, Markov localization for mobile robots in dynamic environments, J. of Artif. Intell. Res. 11 (1999), 391-427. https://doi.org/10.1613/jair.616
  29. J. Gonzalez et al., Mobile robot localization based on ultra-wide-band ranging: A particle filter approach, Rob. Auton. Syst. 57 (2009), 496-507. https://doi.org/10.1016/j.robot.2008.10.022
  30. A. Gil et al., Multi-robot visual SLAM using a Rao-Blackwellized particle filter, Rob. Auton. Syst. 58 (2010), 68-80. https://doi.org/10.1016/j.robot.2009.07.026
  31. J. M. Pak et al., Improving reliability of particle filter-based localization in wireless sensor networks via hybrid particle/FIR filtering, IEEE Trans. Ind. Inf. 11 (2015), 1089-1098. https://doi.org/10.1109/TII.2015.2462771
  32. J. Jung, S.-M. Lee, and H. Myung, Indoor mobile robot localization and mapping based on ambient magnetic fields and aiding radio sources, IEEE Trans. Instrum. Meas. 64 (2015), 1922-1934. https://doi.org/10.1109/TIM.2014.2366273
  33. J. Wang, P. Wang, and Z. Chen, A novel qualitative motion model based probabilistic indoor global localization method, Inf. Sci. 429 (2018), 284-295. https://doi.org/10.1016/j.ins.2017.11.025
  34. H. Jo et al., Efficient grid-based Rao-Blackwellized particle filter SLAM with inter-particle map sharing, IEEE/ASME Trans. Mechatronics 23 (2018), 714-724. https://doi.org/10.1109/TMECH.2018.2795252
  35. S. Yin and X. Zhu, Intelligent particle filter and its application to fault detection of nonlinear system, IEEE Trans. Ind. Electron. 62 (2015), 3852-3861. https://doi.org/10.1109/TIE.2015.2399396
  36. D. Fox, Adapting the sample size in particle filters through KLD-sampling, Int. J. Rob. Res. 22 (2003), 985-1003. https://doi.org/10.1177/0278364903022012001
  37. A. W. Li and G. S. Bastos, A hybrid self-adaptive particle filter through KLD-sampling and SAMCL, in Proc. Int. Conf. Adv. Robot., Hong Kong, China, July 2017, pp. 1949-1953.
  38. R. P. Guan, B. Ristic, and L. Wang, Combining KLD-sampling with gmapping proposal for grid-based Monte Carlo localization of a moving robot, in Proc Int. Conf. Inf. Fusion, Xian, China, July 2017, pp. 1-8.
  39. Y. Zhang et al., Particle swarm optimization-based minimum residual algorithm for mobile robot localization in indoor environment, Int. J. Adv. Rob. Syst. 14 (2017), no. 5, 1-9.
  40. C.-H. Chien et al, Global localization of Monte Carlo localization based on multi-objective particle swarm optimization, in Proc. IEEE Int. Conf. Consumer Electron., Berlin, Germany, Sept. 2016, pp. 96-97.
  41. G. Tong, Z. Fang, and X. Xu, A particle swarm optimized particle filter for nonlinear system state estimation, in Proc, Int. Conf. Evolutionary Comput., Vancouver, Canada, July 2006, pp. 438-442.
  42. Y. Li et al., Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems, Inf. Sci. 293 (2015), 370-382. https://doi.org/10.1016/j.ins.2014.09.030
  43. T. Van Erven and P. Harremos, Renyi divergence and Kullback-Leibler divergence, IEEE Trans. Inf. Theory 60 (2014), 3797-3820. https://doi.org/10.1109/TIT.2014.2320500
  44. T. Li, M. Bolic, and P. M. Djuric, Resampling methods for particle filtering: Classification, implementation, and strategies, IEEE Signal Process. Maga. 32 (2015), 70-86.
  45. Y. Suh and H. Kim, Feature compensation combining SNR-dependent feature reconstruction and class histogram equalization, ETRI J. 30 (2008), 753-755. https://doi.org/10.4218/etrij.08.0208.0147
  46. Y.-C. Lee, B. Park, and S. Park, Coarse-to-fine robot localization method using radio fingerprint and particle filter, in Proc. IEEE Int. Conf. Autom. Sci. Eng., Taipei, Taiwan, Aug. 2014, pp. 290-296.
  47. T. Back, D. B. Fogel, and Z. Michalewicz, In Evolutionary 1: Basic Algorithms and Operators, Vol 1, CRC Press, Bristol, 2000.
  48. N. Hansen and A. Ostermeier, Completely derandomized selfadaptation in evolution strategies, Evol. Comput. 9 (2001), 159-195. https://doi.org/10.1162/106365601750190398
  49. Y. Shu et al., Gradient-based fingerprinting for indoor localization and tracking, IEEE Trans. Ind. Electron. 63 (2016), 2424-2433. https://doi.org/10.1109/TIE.2015.2509917