Abstract
The demand for construction site automation with mobile robots is increasing due to its advantages in potential cost-saving, productivity, and safety. To be realistically deployed in construction sites, mobile robots must be capable of navigating in unstructured and cluttered environments. Furthermore, mobile robots should recognize both static and dynamic obstacles to determine drivable paths. However, existing robot navigation methods are not suitable for construction applications due to the challenging environmental conditions in construction sites. This study introduces an autonomous as-is 3D spatial data collection and perception method for mobile robots specifically aimed for construction job sites with many spatial uncertainties. The proposed Simultaneous Localization and Mapping (SLAM)-based navigation and object recognition methods were implemented and tested with a custom-designed mobile robot platform, Ground Robot for Mapping Infrastructure (GRoMI), which uses multiple laser scanners and a camera to sense and build a 3D environment map. Since SLAM did not detect uneven surface conditions and spatiotemporal objects on the ground, an obstacle detection algorithm was developed to recognize and avoid obstacles and the highly uneven terrain in real time. Given the 3D real-time scan map generated by 3D laser scanners, a path-finding algorithm was developed for autonomous navigation in an unknown environment with obstacles. Overall, the 3D color-mapped point clouds of construction sites generated by GRoMI were of sufficient quality to be used for many construction management applications such as construction progress monitoring, safety hazard identification, and defect detection.
Keywords
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Cho, Y.K., Wang, C., Tang, P., Haas, C.T.: Target-focused local workspace modeling for construction automation applications. J. Comput. Civ. Eng. 26(5), 661–670 (2012)
Pătrăucean, V., Armeni, I., Nahangi, M., Yeung, J., Brilakis, I., Haas, C.: State of research in automatic as-built modelling. Adv. Eng. Inform. 29, 162–171 (2015)
Chen, J., Cho, Y.K.: Real-time 3D mobile mapping for the built environment. In: 33rd International Symposium on Automation and Robotics in Construction (2016)
Kim, P., Chen, J., Cho, Y.K.: SLAM-driven robotic mapping and registration of 3D point clouds. Autom. Constr. 89C, 38–48 (2018)
Koenig, S., Tovey, C., Smirnov, Y.: Performance bounds for planning in unknown terrain. Artif. Intell. 147(1–2), 253–279 (2003)
Ng, J.: A Practical Comparison of Robot Path Planning Algorithms given only Local Information (2012)
Lumelsky, V.J., Stepanov, A.: Dynamic path planning for a mobile automaton with limited information on the environment. IEEE Trans. Autom. Contr. 31(11), 1058–1063 (1986)
Borenstein, J., Koren, Y.: Real-time obstacle avoidance for fast mobile robots in cluttered environments. In: IEEE International Conference on Robotics and Automation, pp. 572–577, May 1990
Sobh, T., Xiong, X.: Prototyping of Robotic Systems: Applications of Design and Implementation, p. 498. Hershey, Information Science Reference (2012)
Fox, D., Wolfram, B., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4, 1–23 (1997)
Saranrittichai, P., Niparnan, N., Sudsang, A.: Robust local obstacle avoidance for mobile robot based on Dynamic Window approach. In: 2013 10th International Conference on Electrical Engineering, Computer, Telecommunications and Information Technology ECTI-CON 2013, no. 1, pp. 4–7 (2013)
Chi, S., Caldas, C.H.: Automated object identification using optical video cameras on construction sites. Comput. Civ. Infrastruct. Eng. 26(5), 368–380 (2011)
Tang, P., Akinci, B., Huber, D.: Semi-automated as-built modeling of light rail system guide beams. In: Proceedings of Construction Research Congress 2010, vol. 373, no. 41109 (2010)
Jung, J., Hong, S., Yoon, S., Kim, J., Heo, J.: Automated 3D wireframe modeling of indoor structures from point clouds using constrained least-squares adjustment for as-built BIM. J. Comput. Civ. Eng. 30(1), 4015074 (2015)
Siebert, S., Teizer, J.: Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system. Autom. Constr. 41, 1–14 (2014)
Yang, J., Park, M.-W., Vela, P.A., Golparvar-Fard, M.: Construction performance monitoring via still images, time-lapse photos, and video streams: now, tomorrow, and the future. Adv. Eng. Inform. 29(2), 211–224 (2015)
Wang, C., Cho, Y.K.: Performance test for rapid surface modeling of dynamic construction equipment from laser scanner data. In: ISARC Proceedings (2014)
Dimitrov, A., Golparvar-Fard, M.: Segmentation of building point cloud models including detailed architectural/structural features and MEP systems. Autom. Constr. 51(C), 32–45 (2015)
Wang, C., Cho, Y.K., Kim, C.: Automatic BIM component extraction from point clouds of existing buildings for sustainability applications. Autom. Constr. 56, 1–13 (2015)
Bosché, F., Ahmed, M., Turkan, Y., Haas, C.T., Haas, R.: The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: the case of cylindrical MEP components. Autom. Constr. 49, 201–213 (2015)
Chen, J., Fang, Y., Cho, Y.K., Kim, C.: Principal axes descriptor for automated construction-equipment classification from point clouds. J. Comput. Civ. Eng. 31, 1–12 (2016)
Pu, S., Rutzinger, M., Vosselman, G., Oude Elberink, S.: Recognizing basic structures from mobile laser scanning data for road inventory studies. ISPRS J. Photogramm. Remote Sens. 66(6 SUPPL), S28–S39 (2011)
Kim, P., Chen, J., Cho, Y.K.: Robotic sensing and object recognition from thermal-mapped point clouds. Int. J. Intell. Robot. Appl. 1(3), 243–254 (2017)
Freda, L., Oriolo, G.: Frontier-based probabilistic strategies for sensor-based exploration. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 3881–3887 (2005)
Kohlbrecher, S., Von Stryk, O., Meyer, J., Klingauf, U.: A flexible and scalable SLAM system with full 3D motion estimation. In: Proceedings of the 2011 IEEE International Symposium Safety, Security, and Rescue Robotics, Kyoto, Japan, 1–5 November, pp. 155–160 (2011)
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5(1), 90–98 (1986)
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Kim, P., Chen, J., Kim, J., Cho, Y.K. (2018). SLAM-Driven Intelligent Autonomous Mobile Robot Navigation for Construction Applications. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_14
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DOI: https://doi.org/10.1007/978-3-319-91635-4_14
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