Advertisement

Deeper in BLUE

Development of a roBot for Localization in Unstructured Environments
  • Iván del PinoEmail author
  • Miguel Á. Muñoz-Bañon
  • Saúl Cova-Rocamora
  • Miguel Á. Contreras
  • Francisco A. Candelas
  • Fernando Torres
Article
  • 17 Downloads

Abstract

Despite the progress that has been made with simulators and the existence of datasets, real experimental platforms are, and will continue to be necessary. Well-designed research platforms that produce reliable results and are easy to operate and debug make all the difference in research productivity. In this paper, we show the works that turned a stock electric cart into a research robot called BLUE. It provides a ROS interface that allows real-time control, monitoring, and adjustment of the system. We provide a quantitative performance evaluation, and a GitHub repository that contains all the information required to replicate the process.

Keywords

Unmanned ground vehicle Mobile robot Localization GNSS SLAM Low-level control Extended Kalman filter ROS 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

We would like to thank Juan Andrade-Cetto and Joan Solà from the Institut de Robòtica i Informàtica Industrial (IRI) for their advice and valuable discussion. We also would like to thank Mr. Antonio López Moraga, part time instructor in the Department of Civil Engineering at the University of Alicante, for his help in the calibration of the GNSS receivers, and María Cutillas Muñoz for the excellent concept illustration in Fig. ??.

References

  1. 1.
    ABB: Technical Guide No. 100: High Performance Drives - Speed and torque regulation ABB Industrial Systems (1996)Google Scholar
  2. 2.
    Agarwal, A., et al.: Design and development of an affordable autonomous vehicle for bike lanes. Ph.D. thesis, Massachusetts Institute of Technology (2018)Google Scholar
  3. 3.
    Aulinas, J., Petillot, Y.R., Salvi, J., Lladó, X.: The slam problem: A survey. CCIA 184(1), 363–371 (2008)Google Scholar
  4. 4.
    AUROVA: Clear repository. http://github.com/AUROVA-LAB/CLEAR (2018)
  5. 5.
    Berger, C.: From a competition for self-driving miniature cars to a standardized experimental platform: concept, models, architecture, and evaluation. arXiv:http://arXiv.org/abs/1406.7768 (2014)
  6. 6.
    Bogue, R.: Robots poised to revolutionise agriculture. Indus. Robot: Int. J. 43(5), 450–456 (2016)CrossRefGoogle Scholar
  7. 7.
    Buehler, M., Iagnemma, K., Singh, S.: The 2005 DARPA Grand Challenge: The Great Robot Race, vol. 36. Springer (2007)Google Scholar
  8. 8.
    Buehler, M., Iagnemma, K., Singh, S.: The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, vol. 56. Springer (2009)Google Scholar
  9. 9.
    Carlson, J.: Mapping Large, Urban Environments with GPS-aided Slam. Carnegie Mellon University (2010)Google Scholar
  10. 10.
    Champeny-Bares, L., Coppersmith, S., Dowling, K.: The terregator mobile robot. Tech. rep., Carnegie-Mellon Univ Pittsburgh PA Robotics Inst (1991)Google Scholar
  11. 11.
    Cup, A.A.D.: Audi autonomous driving cup (2016)Google Scholar
  12. 12.
    Curtis: Manual for Curtis PMC 1223-1237 MultiMode Motor Controllers. Curtis Instruments (2000)Google Scholar
  13. 13.
    Dickmanns, E.D., Behringer, R., Dickmanns, D., Hildebrandt, T., Maurer, M., Thomanek, F., Schiehlen, J.: The seeing passenger car ‘vamors-p’. In: Intelligent Vehicles’ 94 Symposium, Proceedings of the, pp. 68–73. IEEE (1994)Google Scholar
  14. 14.
    Dickmanns, E.D., Zapp, A.: Autonomous high speed road vehicle guidance by computer vision1. IFAC Proc. 20(5), 221–226 (1987)CrossRefGoogle Scholar
  15. 15.
    Dickmanns, E.D., et al.: Vehicles capable of dynamic vision. In: IJCAI, vol. 97, pp. 1577–1592 (1997)Google Scholar
  16. 16.
    Rohmer, E.S.P.N., Singh, M.F.: V-rep: A versatile and scalable robot simulation framework. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS) (2013)Google Scholar
  17. 17.
    Echeverria, G., Lassabe, N., Degroote, A., Lemaignan, S.: Modular open robots simulation engine: Morse. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 46–51. IEEE (2011)Google Scholar
  18. 18.
    Fernandes, L.C., Souza, J.R., Pessin, G., Shinzato, P.Y., Sales, D., Mendes, C., Prado, M., Klaser, R., Magalhaes, A.C., Hata, A., et al: Carina intelligent robotic car: Architectural design and applications. J. Syst. Archit. 60(4), 372–392 (2014)CrossRefGoogle Scholar
  19. 19.
    From, P.J., Grimstad, L., Hanheide, M., Pearson, S., Cielniak, G.: Rasberry-robotic and autonomous systems for berry production. Mech. Eng. Mag. Select Articles 140(6), S14–S18 (2018)Google Scholar
  20. 20.
    Garcia, E., Contreras, J., Olmedo, E., Vargas, H., Rosales, L., Candia, F.: Development of a mobile robot prototype based on an embedded system for mapping generation and path planning-image processing & communication-ipc 2018. In: International Conference on Image Processing and Communications, pp. 11–19. Springer (2018)Google Scholar
  21. 21.
    Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)CrossRefGoogle Scholar
  22. 22.
    Godoy, J., Pérez, J., Onieva, E., Villagrá, J., Milanés, V., Haber, R.: A driverless vehicle demonstration on motorways and in urban environments. Transport 30(3), 253–263 (2015)CrossRefGoogle Scholar
  23. 23.
    Gomes, L.: When will google’s self-driving car really be ready? It depends on where you live and what you mean by “ready” [news]. IEEE Spectr. 53(5), 13–14 (2016)CrossRefGoogle Scholar
  24. 24.
    González, E.J., Acosta, L., Hamilton, A., Felipe, J., Sigut, M., Toledo, J., Arnay, R.: Towards a multiagent approach for the verdino prototype. In: International Work-Conference on Artificial Neural Networks, pp. 21–24. Springer (2009)Google Scholar
  25. 25.
    Goto, Y., Stentz, A.: Mobile robot navigation: The CMU system. In: IEEE expert. Citeseer (1987)Google Scholar
  26. 26.
    Grimstad, L., From, P.J.: The thorvald ii agricultural robotic system. Robotics 6(4), 24 (2017)CrossRefGoogle Scholar
  27. 27.
    Grissetti, G., Stachniss, C., Burgard, W.: GmappingGoogle Scholar
  28. 28.
    Guzmán, R., Navarro, R., Cantero, M., Ariño, J.: Robotnik—professional service robotics applications with ros (2). In: Robot Operating System (ROS), pp. 419–447. Springer (2017)Google Scholar
  29. 29.
    Hebert, M.: Outdoor scene analysis using range data. In: 1986 IEEE International Conference on Robotics and Automation. Proceedings, vol. 3, pp. 1426–1432. IEEE (1986)Google Scholar
  30. 30.
    Hernández, D., Trejo, H., Ordoñez, E.: Development of an exploration land robot using low-cost and open source platforms for educational purposes. In: Journal of Physics: Conference Series, vol. 582, p. 012007. IOP Publishing (2015)Google Scholar
  31. 31.
    Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., Lin, Y., Yang, R.: The apolloscape dataset for autonomous driving. arXiv:1803.06184 (2018)
  32. 32.
    Jochem, T., Pomerleau, D.: No Hands Across America Official Press Release. Carnegie Mellon University (1995)Google Scholar
  33. 33.
    Kayacan, E., Kayacan, E., Ramon, H., Kaynak, O., Saeys, W.: Towards agrobots: Trajectory control of an autonomous tractor using type-2 fuzzy logic controllers. IEEE/ASME Trans. Mechatron. 20(1), 287–298 (2015)CrossRefGoogle Scholar
  34. 34.
    Kemp, C.C., Edsinger, A., Torres-Jara, E.: Challenges for robot manipulation in human environments [grand challenges of robotics]. IEEE Robot. Autom. Mag. 14(1), 20–29 (2007)CrossRefGoogle Scholar
  35. 35.
    Ko, M.H., Ryuh, B.S., Kim, K.C., Suprem, A., Mahalik, N.P.: Autonomous greenhouse mobile robot driving strategies from system integration perspective: Review and application. IEEE/ASME Trans. Mechatron. 20 (4), 1705–1716 (2015)CrossRefGoogle Scholar
  36. 36.
    Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings, vol. 3, pp. 2149–2154. IEEE (2004)Google Scholar
  37. 37.
    Kweon, I.S., Goto, Y., Matsuzaki, K., Obatake, T.: CMU sidewalk navigation system: A blackboard-based outdoor navigation system using sensor fusion with colored-range images. In: Fall Joint Computer Conference. IEEE (1986)Google Scholar
  38. 38.
    Kweon, I.S., Hebvert, M., Kanade, T.: Sensor fusion of range and reflectance data for outdoor scene analysis (1988)Google Scholar
  39. 39.
    Litman, T.: Autonomous vehicle implementation predictions. Victoria Transport Policy Institute 28 (2014)Google Scholar
  40. 40.
    Maddern, W., Pascoe, G., Linegar, C., Newman, P.: 1 year, 1000 km: The Oxford robotcar dataset. Int. J. Robot. Res. 36(1), 3–15 (2017)CrossRefGoogle Scholar
  41. 41.
    Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., Dewhurst, M.: A future that works: Automation, employment, and productivity. Tech. rep., McKinsey Global Institute (2017)Google Scholar
  42. 42.
    Marin, L., Vallés, M., Soriano, A., Valera, A., Albertos, P.: Event-based localization in Ackermann steering limited resource mobile robots. IEEE/ASME Trans. Mechatron. 19(4), 1171–1182 (2014)CrossRefGoogle Scholar
  43. 43.
    Markom, M.A., Shukor, S.A.A., Adom, A.H., Tan, E.S.M.M., Shakaff, A.Y.M.: Indoor scanning and mapping using mobile robot and rp lidar. IEEE IRIS (2015)Google Scholar
  44. 44.
    Michel, O.: Cyberbotics ltd. webots: Professional mobile robot simulation. Int. J. Adv. Robot. Syst. 1(1), 5 (2004)CrossRefGoogle Scholar
  45. 45.
    Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., Hilden, T., Hoffmann, G., Huhnke, B., et al.: Junior: The Stanford entry in the urban challenge. J. Field Robot. 25(9), 569–597 (2008)CrossRefGoogle Scholar
  46. 46.
    Montes, H., Salinas, C., Fernández, R., Armada, M.: An experimental platform for autonomous bus development. Appl. Sci. 7(11), 1131 (2017)CrossRefGoogle Scholar
  47. 47.
    Moore, T., Stouch, D.: A generalized extended Kalman filter implementation for the robot operating system. In: Intelligent Autonomous Systems, vol. 13, pp. 335–348. Springer (2016)Google Scholar
  48. 48.
    Nagaty, A., Saeedi, S., Thibault, C., Seto, M., Li, H.: Control and navigation framework for quadrotor helicopters. J. Intell. Robot. Syst. 70(1–4), 1–12 (2013)CrossRefGoogle Scholar
  49. 49.
    Nilsson, N.J.: A mobile automaton: An application of artificial intelligence techniques. Tech. rep., Sri International Menlo Park CA Artificial Intelligence Center (1969)Google Scholar
  50. 50.
    Oksanen, T.: Accuracy and performance experiences of four wheel steered autonomous agricultural tractor in sowing operation. In: Field and Service Robotics, pp. 425–438. Springer (2015)Google Scholar
  51. 51.
    Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., Mathews, N., Ferrante, E., Di Caro, G., Ducatelle, F., et al.: Argos: A modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)CrossRefGoogle Scholar
  52. 52.
    del Pino, I., Muñoz Bañón, M.A., Contreras, M. Á, Cova, S., Candelas, F.A., Torres, F.: Speed estimation for control of an unmanned ground vehicle using extremely low resolution sensors. In: To appear in 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO) (2018)Google Scholar
  53. 53.
    del Pino, I., Cova, S., Contreras, M. Á, Candelas, F.A., Torres, F.: Presenting blue: A robot for localization in unstructured environments. In: 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 130–135. IEEE (2018)Google Scholar
  54. 54.
    del Pino, I., Vaquero, V., Masini, B., Solà, J., Moreno-Noguer, F., Sanfeliu, A., Andrade-Cetto, J.: Low resolution lidar-based multi-object tracking for driving applications. In: Iberian Robotics conference, pp. 287–298. Springer (2017)Google Scholar
  55. 55.
    Pomerleau, D., Jochem, T.: Rapidly adapting machine vision for automated vehicle steering. IEEE Expert 11(2), 19–27 (1996)CrossRefGoogle Scholar
  56. 56.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: Ros: An open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 5. Kobe (2009)Google Scholar
  57. 57.
    ROS: List of robots supporting ros. http://robots.ros.org (2018)
  58. 58.
    Sukkarieh, S., Nebot, E.M., Durrant-Whyte, H.F.: A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Trans. Robot. Autom. 15(3), 572–578 (1999)CrossRefGoogle Scholar
  59. 59.
    Shimchik, I., Sagitov, A., Afanasyev, I., Matsuno, F., Magid, E.: Golf cart prototype development and navigation simulation using ros and gazebo. In: MATEC Web of Conferences, vol. 75, p. 09005. EDP Sciences (2016)Google Scholar
  60. 60.
    Skog, I., Handel, P.: In-car positioning and navigation technologies—a survey. IEEE Trans. Intell. Transp. Syst. 10(1), 4–21 (2009)CrossRefGoogle Scholar
  61. 61.
    Skrzypczyński, P.: Mobile robot localization: where we are and what are the challenges? In: International Conference Automation, pp. 249–267. Springer (2017)Google Scholar
  62. 62.
    Sola, J.: Simultaneous localization and mapping with the extended Kalman filter. Avery quick guide with MATLAB code (2013)Google Scholar
  63. 63.
    Staranowicz, A., Mariottini, G.L.: A survey and comparison of commercial and open-source robotic simulator software. In: Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments, p. 56. ACM (2011)Google Scholar
  64. 64.
    Thrun, S.: Toward robotic cars. Commun. ACM 53(4), 99–106 (2010)CrossRefGoogle Scholar
  65. 65.
    Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., et al.: Stanley: The robot that won the darpa grand challenge. J. Field Robot. 23(9), 661–692 (2006)CrossRefGoogle Scholar
  66. 66.
    Toledo, J., Piñeiro, J.D., Arnay, R., Acosta, D., Acosta, L.: Improving odometric accuracy for an autonomous electric cart. Sensors 18(1), 200 (2018)CrossRefGoogle Scholar
  67. 67.
    Torres, P.M., Gonçalves, P.J.: Robotic platform for autonomous driving competition using ros. Roman. Rev. Precis. Mech. Opt. Mechatron. 45, 114 (2014)Google Scholar
  68. 68.
    Turk, M.A., Morgenthaler, D.G., Gremban, K.D., Marra, M.: Vits-a vision system for autonomous land vehicle navigation. IEEE Trans. Pattern Anal. Mach. Intell. 10(3), 342–361 (1988)CrossRefGoogle Scholar
  69. 69.
    Vaquero, V., del Pino, I., Moreno-Noguer, F., Solà, J., Sanfeliu, A., Andrade-Cetto, J.: Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios. In: European Conference on Mobile Robotics, 2017. (ECMR-2017). IEEE (2017)Google Scholar
  70. 70.
    Vaughan, R.T., Gerkey, B.P.: Reusable robot software and the player/stage project. In: Brugali, D. (ed.) Software Engineering for Experimental Robotics, pp 267–289. Springer, Berlin (2007)Google Scholar
  71. 71.
    Ye, Y., Wang, Z., Jones, D., He, L., Taylor, M.E., Hollinger, G.A., Zhang, Q.: Bin-dog: A robotic platform for bin management in orchards. Robotics 6(2), 12 (2017)CrossRefGoogle Scholar
  72. 72.
    Yin, X., Du, J., Noguchi, N., Yang, T., Jin, C.: Development of autonomous navigation system for rice transplanter. Int. J. Agri. Biol. Eng. 11(6), 89–94 (2018)Google Scholar
  73. 73.
    Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., Darrell, T.: Bdd100k: A diverse driving video database with scalable annotation tooling. ArXiv e-prints (2018)Google Scholar
  74. 74.
    Ziebinski, A., Cupek, R., Erdogan, H., Waechter, S.: A survey of adas technologies for the future perspective of sensor fusion. In: International Conference on Computational Collective Intelligence, pp. 135–146. Springer (2016)Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.AUROVA: Group of Automation, Robotics and Computer VisionUniversity of AlicanteAlicanteSpain

Personalised recommendations