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ROS Integration of an Instrumented Bobcat T190 for the SEMFIRE Project

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Robot Operating System (ROS)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 962))

Abstract

Forestry and agricultural robotics are growing areas of research within the field of Robotics. Recent developments in planning, perception and mobility in unstructured outdoor environments have allowed the proliferation of innovative autonomous machines. The SEMFIRE project proposes a robotic system to support the removal of flammable material in forests, thus assisting in landscaping maintenance tasks and avoiding the deflagration of wildfires. In this work, we describe work in progress on the development of the Ranger, a large heavy-duty forestry machine based on the well-known Bobcat T190, which is the main actor of SEMFIRE. We present the design of the machine, which has been expanded with several sensors, its full integration in the Robot Operating System, as well as preliminary results.

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Notes

  1. 1.

    http://www.lslidar.com/product/leida/MX/index.html.

  2. 2.

    https://www.intelrealsense.com/depth-camera-d415/.

  3. 3.

    https://www.flir.com/products/ax8-automation/.

  4. 4.

    https://www.edmundoptics.eu/p/c2420-23-color-dalsa-genie-nano-poe-camera/4059/.

  5. 5.

    https://emlid.com/reachrs/.

  6. 6.

    https://redshiftlabs.com.au/product/um7-orientation-sensor/.

  7. 7.

    https://up-board.org/.

  8. 8.

    https://www.microchip.com/wwwproducts/en/MCP2551.

  9. 9.

    https://www.tp-link.com/pt/business-networking/unmanaged-switch/tl-sg108/.

  10. 10.

    https://www.asus.com/Networking/4G-AC68U/.

  11. 11.

    http://www.ros.org.

  12. 12.

    http://wiki.ros.org/rosserial.

  13. 13.

    https://github.com/IntelRealSense/realsense-ros.

  14. 14.

    https://github.com/enwaytech/reach_rs_ros_driver.

  15. 15.

    https://github.com/ros-drivers/um7.

  16. 16.

    https://github.com/tongsky723/lslidar_C16.

  17. 17.

    The UWB system ROS driver is currently not publicly available.

  18. 18.

    https://github.com/davidbsp/flir_ax8_simple_driver.

  19. 19.

    http://www.ab-soft.com/activegige.php.

  20. 20.

    https://www.emva.org/standards-technology/genicam/.

  21. 21.

    NIR stands for the Near Infrared channel, commonly used in multispectral imaging.

  22. 22.

    https://github.com/davidbsp/dalsa_genie_nano_c2420.

  23. 23.

    http://moveit.ros.org.

  24. 24.

    http://wiki.ros.org/tf/Overview/Transformations.

  25. 25.

    http://docs.ros.org/melodic/api/pcl_ros/html/classpcl__ros_1_1CropBox.html.

  26. 26.

    http://wiki.ros.org/rosbag.

  27. 27.

    http://wiki.ros.org/camera_calibration.

  28. 28.

    http://wiki.ros.org/octomap_mapping.

  29. 29.

    http://wiki.ros.org/move_base.

  30. 30.

    https://github.com/leggedrobotics/traversability_estimation.

  31. 31.

    https://github.com/ANYbotics/elevation_mapping.

References

  1. J. San-Miguel-Ayanz, E. Schulte, G. Schmuck, A. Camia, P. Strobl, G. Liberta, et al., Comprehensive monitoring of wildfires in Europe: the European forest fire information system (EFFIS), in Approaches to Managing Disaster-Assessing Hazards, Emergencies and Disaster Impacts. European Commission, Joint Research Centre Italy. (IntechOpen, London, 2012)

    Google Scholar 

  2. F.C. Dennis, Fire-resistant landscaping. Fact sheet (Colorado State University. Extension). Natural resources series; no. 6.303 (1999)

    Google Scholar 

  3. M.S. Couceiro, D. Portugal, J.F. Ferreira, R.P. Rocha, SEMFIRE: towards a new generation of forestry maintenance multi-robot systems, in 2019 IEEE/SICE International Symposium on System Integration (SII) (IEEE, Paris, 2019), pp. 270–276

    Google Scholar 

  4. M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, et al., ROS: an open-source robot operating system, in ICRA Workshop on Open Source Software, vol. 3, No. 3.2 (2009), p. 5

    Google Scholar 

  5. K. Vestlund, T. Hellström, Requirements and system design for a robot performing selective cleaning in young forest stands. J. Terramechanics 43(4), 505–525 (2006)

    Article  Google Scholar 

  6. T. Hellström, P. Lärkeryd, T. Nordfjell, O. Ringdahl, Autonomous forest vehicles: historic, envisioned, and state-of-the-art. Int. J. For. Eng. 20(1), 31–38 (2009)

    Google Scholar 

  7. T. Hellström, A. Ostovar, T. Hellström, A. Ostovar, Detection of trees based on quality guided image segmentation, in Proceedings of the Second International RHEA Conference, Madrid, Spain, pp. 21–23

    Google Scholar 

  8. A. Ostovar, T. Hellström, O., Ringdahl, Human detection based on infrared images in forestry environments, in International Conference on Image Analysis and Recognition (Springer, Cham, 2016), pp. 175–182

    Google Scholar 

  9. T. Hellström, O. Ringdahl, A software framework for agricultural and forestry robots. Ind. Robot: An Int. J. (2013)

    Google Scholar 

  10. L. Sängstuvall, D. Bergström, T. Lämås, T. Nordfjell, Simulation of harvester productivity in selective and boom-corridor thinning of young forests. Scand. J. For. Res. 27(1), 56–73 (2012)

    Article  Google Scholar 

  11. M.S. Couceiro, D. Portugal, Swarming in forestry environments: collective exploration and network deployment. Swarm Intelligence-From Concepts to Applications (IET, London, 2018), pp. 323–344

    Google Scholar 

  12. O. Lindroos, O. Ringdahl, P. La Hera, P. Hohnloser, T.H. Hellström, Estimating the position of the harvester head-a key step towards the precision forestry of the future? Croat. J. For. Eng.: J. Theory Appl. For. Eng. 36(2), 147–164 (2015)

    Google Scholar 

  13. C.W. Bac, J. Hemming, E.J. Van Henten, Stem localization of sweet-pepper plants using the support wire as a visual cue. Comput. Electron. Agric. 105, 111–120 (2014)

    Article  Google Scholar 

  14. C.W. Bac, J. Hemming, E.J. Van Henten, Robust pixel-based classification of obstacles for robotic harvesting of sweet-pepper. Comput. Electron. Agric. 96, 148–162 (2013)

    Article  Google Scholar 

  15. G.W. Geerling, M. Labrador-Garcia, J.G.P.W. Clevers, A.M.J. Ragas, A.J.M. Smits, Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data. Int. J. Remote. Sens. 28(19), 4263–4284 (2007)

    Article  Google Scholar 

  16. J. Hemming, T. Rath, PA-Precision agriculture: computer-vision-based weed identification under field conditions using controlled lighting. J. Agric. Eng. Res. 78(3), 233–243 (2001)

    Article  Google Scholar 

  17. D. Albani, D. Nardi, V. Trianni, Field coverage and weed mapping by UAV swarms, in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (IEEE, Canada, 2017), pp. 4319–4325

    Google Scholar 

  18. P. Lottes, R. Khanna, J. Pfeifer, R. Siegwart, C. Stachniss, UAV-based crop and weed classification for smart farming, in 2017 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, Singapore, 2017), pp. 3024–3031

    Google Scholar 

  19. P. Lottes, C. Stachniss, Semi-supervised online visual crop and weed classification in precision farming exploiting plant arrangement, in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (IEEE, Canada, 2017), pp. 5155–5161

    Google Scholar 

  20. A. Milioto, P. Lottes, C. Stachniss, Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge, in CNNs, in 2018 IEEE international conference on robotics and automation (ICRA). (IEEE, Piscataway, 2018), pp. 2229–2235

    Google Scholar 

  21. P. Lottes, J. Behley, A. Milioto, C. Stachniss, Fully convolutional networks with sequential information for robust crop and weed detection in precision farming. IEEE Robot. Autom. Lett. 3(4), 2870–2877 (2018)

    Article  Google Scholar 

  22. J. Hemming, J. Bontsema, C.W. Bac, Y. Edan, B.A.J. van Tuijl, R. Barth, E.J. Pekkeriet, CROPS: intelligent sensing and manipulation for sustainable production and harvesting of high value crops, clever robots for crops: final report sweet-pepper harvesting robot, Wageningen UR (2014)

    Google Scholar 

  23. M. Garrido, A. Ribeiro, P. Barreiro, B. Debilde, P. Balmer, J. Carballido, Safety functional requirements for robot fleets for highly effective agriculture and forestry management (RHEA), in Proceedings of the international conference of agricultural engineering, CIGR-AgEng2012, Valencia, Spain. July 8e12 (2012)

    Google Scholar 

  24. F. Rovira-Más, C. Millot, V. Sáiz-Rubio, Navigation strategies for a vineyard robot, in 2015 ASABE Annual International Meeting (American Society of Agricultural and Biological Engineers, St. Joseph, MI, 2015), p. 1

    Google Scholar 

  25. M. Héder, From NASA to EU: the evolution of the TRL scale in public sector innovation. Innovat. J. 22(2), 1–23 (2017)

    Google Scholar 

  26. C.M. Lopes, J. Graça, G. Victorino, R. Guzmán, A. Torres, M. Reyes, et al., VINBOT-a terrestrial robot for precision viticulture, in I Congresso Luso-Brasileiro de Horticultura (I CLBHort), Lisboa, Portugal, 1-4 de novembro de 2017 (Associação Portuguesa de Horticultura (APH), Lisbon, 2018), pp. 517–523

    Google Scholar 

  27. R. Guzmán, J. Ariño, R. Navarro, C.M. Lopes, J. Graça, M. Reyes, et al., Autonomous hybrid GPS/reactive navigation of an unmanned ground vehicle for precision viticulture-VINBOT, in 62nd German Winegrowers Conference At: Stuttgart (2016)

    Google Scholar 

  28. J. Lisein, A. Michez, H. Claessens, P. Lejeune, Discrimination of deciduous tree species from time series of unmanned aerial system imagery. PLoS One 10(11) (2015)

    Google Scholar 

  29. A. Michez, H. Piégay, J. Lisein, H. Claessens, P. Lejeune, Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environ Monit. Assess. 188(3), 146 (2016)

    Article  Google Scholar 

  30. I. Sa, Z. Chen, M. Popović, R. Khanna, F. Liebisch, J. Nieto, R. Siegwart, weednet: dense semantic weed classification using multispectral images and mav for smart farming. IEEE Robot. Autom. Lett. 3(1), 588–595 (2017)

    Article  Google Scholar 

  31. S. Zhou, J. Xi, M.W. McDaniel, T. Nishihata, P. Salesses, K. Iagnemma, Self-supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain. J. Field Robot. 29(2), 277–297 (2012)

    Article  Google Scholar 

  32. M.W. McDaniel, T. Nishihata, C.A. Brooks, P. Salesses, K. Iagnemma, Terrain classification and identification of tree stems using ground-based LiDAR. J. Field Robot. 29(6), 891–910 (2012)

    Article  Google Scholar 

  33. J.F. Lalonde, N. Vandapel, D.F. Huber, M. Hebert, Natural terrain classification using three-dimensional ladar data for ground robot mobility. J. Field Robot. 23(10), 839–861 (2006)

    Article  Google Scholar 

  34. D.M. Bradley, R. Unnikrishnan, J. Bagnell, Vegetation detection for driving in complex environments, in Proceedings 2007 IEEE International Conference on Robotics and Automation (IEEE, Roma, 2007), pp. 503–508

    Google Scholar 

  35. D. Bradley, S. Thayer, A. Stentz, P. Rander, Vegetation detection for mobile robot navigation. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, Tech. Rep. CMU-RI-TR-04-12 (2004)

    Google Scholar 

  36. C.W. Bac, E.J. van Henten, J. Hemming, Y. Edan, Harvesting robots for high-value crops: State-of-the-art review and challenges ahead. J. Field Robot. 31(6), 888–911 (2014)

    Article  Google Scholar 

  37. ROS Agriculture, http://rosagriculture.org/

  38. V. Trianni, J. IJsselmuiden, R. Haken, The Saga Concept: Swarm Robotics for Agricultural Applications. Technical Report. 2016 (2016), http://laral.istc.cnr.it/saga/wp-content/uploads/2016/09/sagadars2016.pdf. Accessed 23 Aug 2018

    Google Scholar 

  39. S. Tiwari, Y. Zheng, M. Pattinson, M. Campo-Cossio, R. Arnau, D. Obregon, et al., Approach for autonomous robot navigation in greenhouse environment for integrated pest management, in 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) (IEEE, Portland, 2020), pp. 1286–1294

    Google Scholar 

  40. B. Arad, J. Balendonck, R. Barth, O. Ben–Shahar, Y. Edan, T. Hellström, et al., Development of a sweet pepper harvesting robot. J. Field Robot. (2020)

    Google Scholar 

  41. J.F. Ferreira, G.S. Martins, P. Machado, D. Portugal, R.P. Rocha, N. Gonçalves, M.S. Couceiro, Sensing and artificial perception for robots in precision forestry—a survey. J. Field Robot. Wiley 2020. (Under Review)

    Google Scholar 

  42. D. Portugal, J.F. Ferreira, M.S. Couceiro, Requirements specification and integration architecture for perception in a cooperative team of forestry robots, in Proceedings of Towards Autonomous Robotic Systems 2020 (TAROS 2020), University of Nottingham, Nottingham, UK, July 22–24 (2020)

    Google Scholar 

  43. G.S. Martins, J.F. Ferreira, D. Portugal, M.S. Couceiro, MoDSeM: modular framework for distributed semantic mapping, in The 2nd UK-RAS Conference on Embedded Intelligence (UK-RAS19), Loughborough (2019)

    Google Scholar 

  44. G.S. Martins, J.F. Ferreira, D. Portugal, M.S. Couceiro, MoDSeM: towards semantic mapping with distributed robots, in Annual Conference Towards Autonomous Robotic Systems (TAROS 2019), Queen Mary University of London (2019)

    Google Scholar 

  45. P. Machado, J. Bonnell, S. Brandenbourg, J.F. Ferreira, D. Portugal, M.S. Couceiro, Robotics use case scenarios, in Towards Ubiquitous Low-power Image Processing Platforms (TULIPP), ed. by M. Jahre, D. Göhringer, P. Millet (Springer, Berlin, 2020)

    Google Scholar 

  46. M. Mukhandi, D. Portugal, S. Pereira, M.S. Couceiro, A novel solution for securing robot communications based on the MQTT protocol and ROS, in 2019 IEEE/SICE International Symposium on System Integration (SII). (IEEE, Piscataway, 2019), pp. 608–613

    Google Scholar 

  47. E. Marder-Eppstein, E. Berger, T. Foote, B. Gerkey, K. Konolige, The office marathon: robust navigation in an indoor office environment, in 2010 IEEE International Conference on Robotics and Automation. (IEEE, Piscataway, 2010), pp. 300–307

    Google Scholar 

  48. D. Lourenço, J.F. Ferreira, D. Portugal, 3D local planning for a forestry UGV based on terrain gradient and mechanical effort, in Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), Workshop on Perception, Planning and Mobility in Forestry Robotics (WPPMFR 2020), Las Vegas, NV, USA, Oct 25–29 (2020)

    Google Scholar 

  49. P. Fankhauser, M. Bloesch, M. Hutter, Probabilistic terrain mapping for mobile robots with uncertain localization. IEEE Robot. Autom. Lett. 3(4), 3019–3026 (2018)

    Article  Google Scholar 

  50. A.K. Nasir, A.G. Araújo, M.S. Couceiro, Localization and navigation assessment of a heavy-duty field robot, in Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), Workshop on Perception, Planning and Mobility in Forestry Robotics (WPPMFR 2020), Las Vegas, NV, USA, Oct 25–29 (2020)

    Google Scholar 

  51. M.E. Andrada, J.F. Ferreira, D. Portugal, M. Couceiro, Testing different CNN architectures for semantic segmentation for landscaping with forestry robotics, in Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), Workshop on Perception, Planning and Mobility in Forestry Robotics (WPPMFR 2020), Las Vegas, NV, USA, Oct 25–29 (2020)

    Google Scholar 

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Acknowledgements

We are sincerely thankful to Gonçalo S. Martins, Pedro Machado, Dora Lourenço, Hugo Marques, Miguel Girão, Daryna Datsenko, João Aguizo, Ahmad Kamal Nasir, Tolga Han and the ROS community for their contributions to this work.

This work was supported by the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269) and the Centre of Operations for Rethinking Engineering (CORE, ref. CENTRO-01-0247-FEDER-037082) research projects co-funded by the “Agência Nacional de Inovação” within the Portugal2020 programme.

Author information

Authors and Affiliations

  1. Institute of Systems and Robotics, University of Coimbra—Pólo II, Coimbra, Portugal

    David Portugal, Maria Eduarda Andrada & João Filipe Ferreira

  2. Ingeniarius, Ltd., R. Coronel Veiga Simão, Edifício B CTCV, 3025-307, Coimbra, Portugal

    André G. Araújo & Micael S. Couceiro

  3. Computational Neuroscience and Cognitive Robotics Group, School of Science and Technology, Nottingham Trent University, Nottingham, UK

    João Filipe Ferreira

Authors
  1. David Portugal
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  2. Maria Eduarda Andrada
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  3. André G. Araújo
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  4. Micael S. Couceiro
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  5. João Filipe Ferreira
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Corresponding author

Correspondence to David Portugal .

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Editors and Affiliations

  1. College of Computer Science and Information Systems, Prince Sultan University, Riyadh, Saudi Arabia

    Anis Koubaa

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Portugal, D., Andrada, M.E., Araújo, A.G., Couceiro, M.S., Ferreira, J.F. (2021). ROS Integration of an Instrumented Bobcat T190 for the SEMFIRE Project. In: Koubaa, A. (eds) Robot Operating System (ROS). Studies in Computational Intelligence, vol 962. Springer, Cham. https://doi.org/10.1007/978-3-030-75472-3_3

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