Rapid Human-Assisted Creation of Bounding Models for Obstacle Avoidance in Construction
: State‐of‐the‐art construction equipment control technology creates the opportunity to implement automated and semiautomated object avoidance for improved safety and efficiency during operation; however, methods for constructing models of local objects or volumes in real‐time are required. A pract...
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| Veröffentlicht in: | Computer-aided civil and infrastructure engineering Jg. 19; H. 1; S. 3 - 15 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Boston, USA and Oxford, UK
Blackwell Publishing, Inc
01.01.2004
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| ISSN: | 1093-9687, 1467-8667 |
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| Abstract | : State‐of‐the‐art construction equipment control technology creates the opportunity to implement automated and semiautomated object avoidance for improved safety and efficiency during operation; however, methods for constructing models of local objects or volumes in real‐time are required. A practical, interactive method for doing so is described here. The method: (1) exploits a human operator's ability to quickly recognize significant objects or clusters of objects in a scene, (2) exploits the operator's ability to acquire sparse range point clouds of the objects quickly, and then (3) renders models, such as planes, boxes, and generalized convex hulls, to be displayed graphically as visual feedback during equipment operation and/or for making proximity calculations in an obstacle detection system. Experiments were performed in which test subjects were asked to model objects of varying complexity and clutter. These models were then compared to control models using a ray‐tracing algorithm to determine the operator's ability to create conservative models that are critical to construction operations. To demonstrate the applicability of the modeling method to obstacle avoidance, a scripted motion robot simulation was conducted using an artificial potential formulation that monitors position (closest point on manipulator link to nearest obstacle) as well as velocity (link inertia). Experimental results indicate that bounding models can be created rapidly and with sufficient accuracy for obstacle avoidance with the aid of human intelligence and that human‐assisted modeling can be very beneficial for real‐time construction equipment control. |
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| AbstractList | State-of-the-art construction equipment control technology creates the opportunity to implement automated and semiautomated object avoidance for improved safety and efficiency during operation; however, methods for constructing models of local objects or volumes in real-time are required. A practical, interactive method for doing so is described here. The method: (1) exploits a human operator's ability to quickly recognize significant objects or clusters of objects in a scene, (2) exploits the operator's ability to acquire sparse range point clouds of the objects quickly, and then (3) renders models, such as planes, boxes, and generalized convex hulls, to be displayed graphically as visual feedback during equipment operation and/or for making proximity calculations in an obstacle detection system. Experiments were performed in which test subjects were asked to model objects of varying complexity and clutter. These models were then compared to control models using a ray-tracing algorithm to determine the operator's ability to create conservative models that are critical to construction operations. To demonstrate the applicability of the modeling method to obstacle avoidance, a scripted motion robot simulation was conducted using an artificial potential formulation that monitors position (closest point on manipulator link to nearest obstacle) as well as velocity (link inertia). Experimental results indicate that bounding models can be created rapidly and with sufficient accuracy for obstacle avoidance with the aid of human intelligence and that human-assisted modeling can be very beneficial for real-time construction equipment control. : State‐of‐the‐art construction equipment control technology creates the opportunity to implement automated and semiautomated object avoidance for improved safety and efficiency during operation; however, methods for constructing models of local objects or volumes in real‐time are required. A practical, interactive method for doing so is described here. The method: (1) exploits a human operator's ability to quickly recognize significant objects or clusters of objects in a scene, (2) exploits the operator's ability to acquire sparse range point clouds of the objects quickly, and then (3) renders models, such as planes, boxes, and generalized convex hulls, to be displayed graphically as visual feedback during equipment operation and/or for making proximity calculations in an obstacle detection system. Experiments were performed in which test subjects were asked to model objects of varying complexity and clutter. These models were then compared to control models using a ray‐tracing algorithm to determine the operator's ability to create conservative models that are critical to construction operations. To demonstrate the applicability of the modeling method to obstacle avoidance, a scripted motion robot simulation was conducted using an artificial potential formulation that monitors position (closest point on manipulator link to nearest obstacle) as well as velocity (link inertia). Experimental results indicate that bounding models can be created rapidly and with sufficient accuracy for obstacle avoidance with the aid of human intelligence and that human‐assisted modeling can be very beneficial for real‐time construction equipment control. |
| Author | Sreenivasan, S. V. Haas, C. McLaughlin, J. Liapi, K. |
| Author_xml | – sequence: 1 givenname: J. surname: McLaughlin fullname: McLaughlin, J. organization: 1 University of Texas, Austin, USA, 2University of Texas, Austin, USA haas@mail.utexas.edu – sequence: 2 givenname: S. V. surname: Sreenivasan fullname: Sreenivasan, S. V. organization: 1 University of Texas, Austin, USA, 2University of Texas, Austin, USA haas@mail.utexas.edu – sequence: 3 givenname: C. surname: Haas fullname: Haas, C. organization: 1 University of Texas, Austin, USA, 2University of Texas, Austin, USA haas@mail.utexas.edu – sequence: 4 givenname: K. surname: Liapi fullname: Liapi, K. organization: 1 University of Texas, Austin, USA, 2University of Texas, Austin, USA haas@mail.utexas.edu |
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| Cites_doi | 10.1111/j.1467-8667.1995.tb00298.x 10.1109/ROBOT.1997.620018 10.1016/S0926-5805(99)00028-X 10.1061/(ASCE)0733-9364(1998)124:4(289) 10.1177/02783649922067708 10.1109/56.2083 10.22260/ISARC2000/0077 10.1061/(ASCE)0733-9364(1996)122:3(212) 10.1145/235815.235821 10.1007/BF00054921 10.1006/ciun.1993.1025 10.1177/027836498600500302 10.1109/ROBOT.2000.845317 10.22260/ISARC2002/0077 10.1061/(ASCE)0733-9364(1997)123:3(318) 10.1023/A:1008914201877 |
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| References_xml | – reference: Gilbert, E. G., Johnson, D. W. & Keerthi, S. S. (1988), A fast procedure for computing the distance between complex objects in three-dimensional space, IEEE Journal of Robotics and Automation, 4(2), 193-203. – reference: Faugeras, O. D. & Hebert, M. (1986), The representation, recognition, and locating of 3D objects, International Journal of Robotics Research, 5(3), 13-51. – reference: Stentz, A., Bares, J., Singh, S. & Rowe, P. (1998), A robotic excavator for autonomous truck loading, Autonomous Robots, 7(2), 175-86. – reference: LeBlond, D., Owen, F., Gibson, G., Haas, C. & Traver, A. (1998), Performance testing and characterization of advanced construction equipment, ASCE Journal of Construction Engineering and Management, 124(4), 289-96. – reference: Lin, K. & Haas, C. (1996), An interactive planning environment for critical operations, ASCE Journal of Construction Engineering and Management, 122(3), 212-22. – reference: Huttenlocher, D. P. & Ullman, S. (1990), Recognizing solid objects by alignment with an image, International Journal of Computer Vision, 5(2), 195-212. – reference: Kim, Y. S. & Haas, C. (2000), A model for automation of infra- structure maintenance using representational forms, Journal of Automation in Construction, 10(1), 57-68. – reference: Barber, C., Dobkin, D. & Huhdanpaa, H. (1996), The Quickhull algorithm for convex hulls, ACM Transactions on Mathematical Software, 22(4), 469-83. – reference: Haung, X. & Bernold, L. (1997), CAD integrated excavation and pipe laying, ASCE, Journal of Construction Engineering and Management, 123(3), 318-23. – reference: Brady, M. (1999), Editorial note, International Journal of Robotics Research, 18(11), 1051-55. – reference: Sabata, B., Arman, F. & Aggarwal, J. K. (1993), Segmentation of 3D range images using pyramidal data structures, CVGIP: Image Understanding, 57(3), 373-87. – reference: Haas, C. T., Skibniewski, M. & Bundy, E. (1995), History of robotics in civil engineering, Microcomputers in Civil Engineering, 10(5), 371-81. – volume: 123 start-page: 318 issue: 3 year: 1997 end-page: 23 article-title: CAD integrated excavation and pipe laying publication-title: ASCE, Journal of Construction Engineering and Management – volume: 57 start-page: 373 issue: 3 year: 1993 end-page: 87 article-title: Segmentation of 3D range images using pyramidal data structures publication-title: CVGIP: Image Understanding – volume: 22 start-page: 469 issue: 4 year: 1996 end-page: 83 article-title: The Quickhull algorithm for convex hulls publication-title: ACM Transactions on Mathematical Software – volume: 5 start-page: 13 issue: 3 year: 1986 end-page: 51 article-title: The representation, recognition, and locating of 3D objects publication-title: International Journal of Robotics Research – year: 2002 – year: 2001 – volume: 122 start-page: 212 issue: 3 year: 1996 end-page: 22 article-title: An interactive planning environment for critical operations publication-title: ASCE Journal of Construction Engineering and Management – volume: 18 start-page: 1051 issue: 11 year: 1999 end-page: 55 article-title: Editorial note publication-title: International Journal of Robotics Research – year: 2000 – volume: 4 start-page: 193 issue: 2 year: 1988 end-page: 203 article-title: A fast procedure for computing the distance between complex objects in three‐dimensional space publication-title: IEEE Journal of Robotics and Automation – volume: 124 start-page: 289 issue: 4 year: 1998 end-page: 96 article-title: Performance testing and characterization of advanced construction equipment publication-title: ASCE Journal of Construction Engineering and Management – volume: 10 start-page: 57 issue: 1 year: 2000 end-page: 68 article-title: A model for automation of infra‐ structure maintenance using representational forms publication-title: Journal of Automation in Construction – start-page: 463 year: 2000 end-page: 77 – year: 1991 – start-page: 72 year: 1997 end-page: 7 – start-page: 3757 year: 2000 end-page: 64 – start-page: 1097 year: 1998 end-page: 107 – volume: 5 start-page: 195 issue: 2 year: 1990 end-page: 212 article-title: Recognizing solid objects by alignment with an image publication-title: International Journal of Computer Vision – volume: 7 start-page: 175 issue: 2 year: 1998 end-page: 86 article-title: A robotic excavator for autonomous truck loading publication-title: Autonomous Robots – volume: 10 start-page: 371 issue: 5 year: 1995 end-page: 81 article-title: History of robotics in civil engineering publication-title: Microcomputers in Civil Engineering – ident: e_1_2_2_11_1 doi: 10.1111/j.1467-8667.1995.tb00298.x – ident: e_1_2_2_17_1 doi: 10.1109/ROBOT.1997.620018 – volume-title: A Survey of Graphical Simulation in Construction: Software, Usage, and Applications year: 1991 ident: e_1_2_2_2_1 – ident: e_1_2_2_14_1 doi: 10.1016/S0926-5805(99)00028-X – ident: e_1_2_2_15_1 doi: 10.1061/(ASCE)0733-9364(1998)124:4(289) – ident: e_1_2_2_4_1 doi: 10.1177/02783649922067708 – ident: e_1_2_2_9_1 doi: 10.1109/56.2083 – ident: e_1_2_2_5_1 doi: 10.22260/ISARC2000/0077 – ident: e_1_2_2_16_1 doi: 10.1061/(ASCE)0733-9364(1996)122:3(212) – ident: e_1_2_2_3_1 doi: 10.1145/235815.235821 – ident: e_1_2_2_12_1 doi: 10.1007/BF00054921 – ident: e_1_2_2_13_1 – ident: e_1_2_2_19_1 doi: 10.1006/ciun.1993.1025 – ident: e_1_2_2_8_1 doi: 10.1177/027836498600500302 – ident: e_1_2_2_6_1 – ident: e_1_2_2_7_1 doi: 10.1109/ROBOT.2000.845317 – ident: e_1_2_2_18_1 doi: 10.22260/ISARC2002/0077 – ident: e_1_2_2_10_1 doi: 10.1061/(ASCE)0733-9364(1997)123:3(318) – ident: e_1_2_2_20_1 doi: 10.1023/A:1008914201877 – ident: e_1_2_2_21_1 |
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