The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - Jan.-March (2012 vol.5)
pp: 11-19
K. Belghith , Dept. of Comput. Sci., Univ. of Sherbrooke, Sherbrooke, QC, Canada
R. Nkambou , Dept. of Comput. Sci., Univ. of Quebec at Montreal, Montreal, QC, Canada
F. Kabanza , Dept. of Comput. Sci., Univ. of Sherbrooke, Sherbrooke, QC, Canada
L. Hartman , Canadian Space Agency, St. Hubert, QC, Canada
ABSTRACT
Roman Tutor is a tutoring system that uses sophisticated domain knowledge to monitor the progress of students and advise them while they are learning how to operate a space telerobotic system. It is intended to help train operators of the Space Station Remote Manipulator System (SSRMS) including astronauts, operators involved in ground-based control of SSRMS and technical support staff. Currently, there is only a single training facility for SSRMS operations and it is heavily scheduled. The training staff time is in heavy demand for teaching students, planning training tasks, developing teaching material, and new teaching tools. For example, all SSRMS simulation exercises are developed by hand and this process requires a lot of staff time. Once in an orbit ISS astronauts currently have only simple web-based material for skill development and maintenance. For long duration space flights, astronauts will require sophisticated simulation tools to maintain skills. Roman Tutor addresses these challenges by providing a portable training tool that can be installed anywhere and anytime to provide “just in time” training. It incorporates a model of the system operations curriculum, a kinematic simulation of the robotics equipment, and the ISS, a high performance path planner and an automatic task demonstration generator. For each element of the curriculum that the student is supposed to master, Roman Tutor generates example tasks for the student to accomplish within the simulation environment and then monitors its progression to provide relevant feedback when needed. Although motivated by the SSRMS application, Roman Tutor remains applicable to any telerobotics system application.
INDEX TERMS
telerobotics, aerospace computing, aerospace simulation, computer based training, control engineering computing, intelligent tutoring systems, Internet, manipulator kinematics, manipulators, path planning, international space station, intelligent simulator, telerobotics training, Roman Tutor, tutoring system, space telerobotic system, space station remote manipulator system, SSRMS ground-based control, technical support staff, training staff time, ISS astronauts, Web-based material, skill development, skill maintenance, portable training tool, just in time training, system operations curriculum, robotics equipment kinematic simulation, path planner, automatic task demonstration generator, Cameras, Robot vision systems, Training, Three dimensional displays, Planning, Trajectory, path planning, telerobotics, aerospace computing, aerospace simulation, computer based training, control engineering computing, intelligent tutoring systems, Internet, manipulator kinematics, manipulators, path planning, international space station, intelligent simulator, telerobotics training, Roman Tutor, tutoring system, space telerobotic system, space station remote manipulator system, SSRMS ground-based control, technical support staff, training staff time, ISS astronauts, Web-based material, skill development, skill maintenance, portable training tool, just in time training, system operations curriculum, robotics equipment kinematic simulation, path planner, automatic task demonstration generator, Cameras, Robot vision systems, Training, Three dimensional displays, Planning, Trajectory, demonstration generation., Telerobotics training, intelligent tutoring, robot manipulation
CITATION
K. Belghith, R. Nkambou, F. Kabanza, L. Hartman, "An Intelligent Simulator for Telerobotics Training", IEEE Transactions on Learning Technologies, vol.5, no. 1, pp. 11-19, Jan.-March 2012, doi:10.1109/TLT.2011.19
REFERENCES
[1] K. Forbus, "Articulate Software for Science and Engineering Education," Smart Machines in Education: The Coming Revolution in Educational Technology, vol. 1, no. 1, pp. 235-267, 2001.
[2] R. Angros, W. Johnson, J. Rickel, and A. Scholer, "Learning Domain Knowledge for Teaching Procedural Skills," Proc. First Int'l Conf. Autonomous Agents and Multiagent Systems (AAMAS), pp. 1372-1378, 2002.
[3] K. VanLehn, "The Advantages of Explicity Representing Problem Spaces," Proc. Ninth Int'l Conf. User Modeling (UM), p. 3, 2003.
[4] R. Crowley, E. Legowski, O. Medvedeva, E. Tseytlin, E. Roh, and D. Jukic, "An Its for Medical Classification Problem-Solving: Effects of Tutoring and Representations," Proc. 12th Int'l Conf. Artificial Intelligence in Education, pp. 192-199, 2005.
[5] H. Simon, "The Structure of Ill Structured Problems," Artificial Intelligence, vol. 4, no. 3, pp. 181-201, 1973.
[6] P. Fournier-Viger, R. Nkambou, and E.M. Nguifo, "Supporting Tutoring Services in Ill-Defined Domains," Advances in Intelligent Tutoring Systems, Nkambou et al., eds., Springer, 2010.
[7] K. Koedinger, J. Anderson, W. Hadley, and M. Mark, "Intelligent Tutoring Goes to School in the Big City," Int'l J. Artificial Intelligence in Education, vol. 8, no. 9, pp. 30-43, 1997.
[8] A. Mitrovic, M. Mayo, P. Suraweera, and B. Martin, "Contraint-Based Tutors: A Success Story," Proc. 14th Int'l Conf. Industrial, Eng. and Other Applications of Applied Intelligent Systems (IEA/AIE), pp. 931-940, 2001.
[9] K. Belghith, F. Kabanza, L. Hartman, and R. Nkambou, "Anytime Dynamic Path-Planning with Flexible Probabilistic Roadmaps," Proc. IEEE Int'l Conf. Robotics and Automation (ICRA), pp. 2372-2377, 2006.
[10] L. Kavraki, P. Svestka, J.C. Latombe, and M. Overmars, "Probabilistic Roadmaps for Path Planning in High Dimensional Configuration Spaces," IEEE Trans. Robotics and Automation, vol. 12, no. 4, pp. 566-580, Aug. 1996.
[11] J. Brown, R. Burton, and F. Zdybel, "A Model-Driven Question-Answering System for Mixed Initiative Computer-Assisted Instruction," IEEE Trans. Systems, Man and Cybernetics, vol. 3, no. 3, pp. 248-257, May 1973.
[12] W. Clancey, Tutoring Rules for Guiding a Case Method Dialogue, D. Sleeman and J. Brown, eds. Academic, 1982.
[13] F. Kabanza, K. Belghith, P. Bellefeuille, B. Auder, and L. Hartman, "Planning 3D Task Demonstrations of a Teleoperated Space Robot Arm," Proc. 18th Int'l Conf. Automated Planning and Scheduling (ICAPS), pp. 164-173, 2008.
[14] F. Bacchus and F. Kabanza, "Using Temporal Logics to Express Search Control Knowledge for Planning," Artificial Intelligence, vol. 116, nos. 1/2, pp. 123-191, 2000.
[15] H. Choset, K. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. Kavraki, and S. Thrun, Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT , 2005.
[16] M. Likhachev, D. Ferguson, G.J. Gordon, A. Stentz, and S. Thrun, "Anytime Search in Dynamic Graphs," Artificial Intelligence, vol. 172, no. 14, pp. 1613-1643, 2008.
[17] M. Saha, J. Latombe, Y. Chang, and F. Prinz, "Finding Narrow Passages with Probabilistic Roadmaps: The Small-Step Retraction Method," J. Autonomous Robots, vol. 19, no. 3, pp. 301-319, 2005.
[18] S.M. Lavalle, Planning Algorithms. Cambridge Univ., 2006.
[19] G. Sanchez and J. Latombe, "A Single-Query Bi-Directional Probabilistic Roadmap Planner with Lazy Collision Checking," Proc. 10th Int'l Symp. Robotics Research (ISRR), pp. 403-417, 2001.
[20] M. Likhachev, D.I. Ferguson, G.J. Gordon, A. Stentz, and S. Thrun, "Anytime Dynamic A$^{\ast}$ : An Anytime, Replanning Algorithm," Proc. 15th Int'l Conf. Automated Planning and Scheduling (ICAPS), pp. 262-271, 2005.
[21] E. Larsen, S. Gottshalk, M. Lin, and D. Manocha, "Fast Proximity Queries with Swept Sphere Volumes," Proc. IEEE Int'l Conf. Robotics and Automation (ICRA), pp. 3719-3726, 2000.
[22] D. Christianson, S. Anderson, L. He, D. Salesin, D. Weld, and C.M.F., "Declarative Camera Control for Automatic Cinematography," Proc. 13th Nat'l Conf. Artificial Intelligence (AAAI/IAAI), pp. 148-155, 1996.
[23] W. Bares, L. Zettlemoyer, D. Rodriguez, and J. Lester, "Task-Sensitive Cinematography Interfaces for Interactive 3D Learning Environments," Proc. Third Int'l Conf. Intelligent User Interfaces (IUI), pp. 81-88, 1998.
[24] D. Friedman and Y. Feldman, "Automated Cinematic Reasoning about Camera Behavior," J. Expert Systems with Applications, vol. 30, no. 4, pp. 694-704, 2006.
[25] A. Jhala and R. Young, "A Discourse Planning Approach to Cinematic Camera Control for Narratives in Virtual Environments," Proc. 20th Nat'l Conf. Artificial Intelligence (AAAI/IAAI), pp. 307-312, 2005.
[26] A. Corbett, K. Koedinger, and J. Anderson, "Intelligent Tutoring Systems," Handbook of Human-Computer Interaction, M. Helander, T.K. Landauer, P. Prabhu, eds., Elsevier Science, 1997.
[27] R. Nkambou, "Modeling the Domain: An Introduction to the Expert Module," Advances in Intelligent Tutoring Systems, Nkambou et al., eds., Springer, 2010.
[28] A. Lesgold, G. Eggan, S. Katz, and G. Rao, "Possibilities for Assessment Using Computer-Based Apprenticeship Environments," Cognitive Approaches to Automated Instruction, J. Regian and V. Shute, eds., Lawrence Eribaum Assoc., 1992.
20 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool