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  • 1.
    Anderson, S. J.
    et al.
    MIT.
    Karumanchi, S. B.
    MIT.
    Iagnemma, Karl
    MIT.
    Constraint-based planning and control for safe, semi-autonomous operation of vehicles2012Inngår i: 2012 IEEE intelligent vehicles symposium: (IV 2012) : Alcala de Henares, Madrid, Spain, 3-7 June 2012, 2012, s. 383-388Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a new approach to semi-autonomous vehicle hazard avoidance and stability control, based on the design and selective enforcement of constraints. This differs from traditional approaches that rely on the planning and tracking of paths. This emphasis on constraints facilitates "minimally-invasive" control for human-machine systems; instead of forcing a human operator to follow an automation-determined path, the constraint-based approach identifies safe homotopies, and allows the operator to navigate freely within them, introducing control action only as necessary to ensure that the vehicle does not violate safety constraints. The method evaluates candidate homotopies based on "restrictiveness", rather than traditional measures of path goodness, and designs and enforces requisite constraints on the human's control commands to ensure that the vehicle never leaves the controllable subset of a desired homotopy. Identification of these homotopic classes in off-road environments is performed using geometric constructs. The goodness of competing homotopies and their associated constraints is then characterized using geometric heuristics. Finally, input limits satisfying homotopy and vehicle dynamic constraints are enforced using threat-based feedback mechanisms to ensure that the vehicle avoids collisions and instability while preserving the human operator's situational awareness and mental models. The methods developed in this work are shown in simulation and experimentally demonstrated in safe, high-speed teleoperation of an unmanned ground vehicle. © 2012 IEEE.

  • 2.
    Anderson, S.
    et al.
    MIT.
    Peters, S.
    MIT.
    Iagnemma, Karl
    MIT.
    Overholt, J.
    US Army Tank Automotive RDE Center (TARDEC).
    Semi-Autonomous Stability Control and Hazard Avoidance for Manned and Unmanned Ground Vehicles2010Inngår i: Proceedings of the 27th Army Science Conference, 2010, s. 1-8Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a method for trajectory planning, threatassessment, and semi-autonomous control of manned andunmanned ground vehicles. A model predictive controlleriteratively replans a stability-optimal trajectory through the saferegion of the environment while a threat assessor and semi-autonomous control law modulate driver and controller inputs tomaintain stability, preserve controllability, and ensure that thevehicle avoids obstacles and hazardous areas. The efficacy of thisapproach in avoiding hazards while accounting for various typesof human error, including errors caused by time delays, isdemonstrated in simulation.

  • 3.
    Anderson, S.
    et al.
    MIT.
    Peters, S.
    MIT, USA.
    Pilutti, T.
    Ford Research Laboratories, Ford Motor Co., Dearborn, MI 48124, United States.
    Tseng, E.
    Ford Research Laboratories, Ford Motor Co., Dearborn, MI 48124, United States.
    Iagnemma, Karl
    MIT.
    Semi-autonomous Avoidance of Moving Hazards for Passenger Vehicles2010Inngår i: Proceedings of the ASME Dynamic Systems and Control Conference--2010: presented at 2010 ASME Dynamic Systems and Control Conference, September 12-15, 2010 Cambridge, Mass., USA, New York: ASME Press, 2010, s. 141-148Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a method for semi-autonomous hazard avoidance in the presence of unknown moving obstacles and unpredictable driver inputs. This method iteratively predicts the motion and anticipated intersection of the host vehicle with both static and dynamic hazards and excludes projected collision states from a traversable corridor. A model predictive controller iteratively replans a stability-optimal trajectory through the navigable region of the environment while a threat assessor and semi-autonomous control law modulate driver and controller inputs to maintain stability, preserve controllability, and ensure safe hazard avoidance. The efficacy of this approach is demonstrated through both simulated and experimental results using a semi-autonomously controlled Jaguar S-Type. Copyright © 2010 by ASME.

  • 4.
    Anderson, Sterling J.
    et al.
    Department of Mechanical Engineering Massachusetts Institute of Technology, Cambridge, MA, USA.
    Karumanchi, Sisir B.
    Department of Mechanical Engineering Massachusetts Institute of Technology, Cambridge, MA, USA.
    Iagnemma, Karl
    Department of Mechanical Engineering Massachusetts Institute of Technology, Cambridge, MA, USA.
    Walker, James M.
    Quantum Signal, LLC, Saline, MI, USA.
    The intelligent copilot: A constraint-based approach to shared-adaptive control of ground vehicles2013Inngår i: IEEE Intelligent Transportation Systems Magazine, ISSN 1939-1390, Vol. 5, nr 2, s. 45-54Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This work presents a new approach to semi-autonomous vehicle hazard avoidance and stability control, based on the design and selective enforcement of constraints. This differs from traditional approaches that rely on the planning and tracking of paths and facilitates minimally-invasive control for human-machine systems. Instead of forcing a human operator to follow an automation-determined path, the constraint-based approach identifies safe homotopies, and allows the operator to navigate freely within them, introducing control action only as necessary to ensure that the vehicle does not violate safety constraints. This method evaluates candidate homotopies based on restrictiveness rather than traditional measures of path goodness, and designs and enforces requisite constraints on the human's control commands to ensure that the vehicle never leaves the controllable subset of a desired homotopy. This paper demonstrates the approach in simulation and characterizes its effect on human teleoperation of unmanned ground vehicles via a 20-user, 600-trial study on an outdoor obstacle course. Aggregated across all drivers and experiments, the constraintbased control system required an average of 43% of the available control authority to reduce collision frequency by 78% relative to traditional teleoperation, increase average speed by 26%, and moderate operator steering commands by 34%. © 2009-2012 IEEE

  • 5.
    Anderson, Sterling J.
    et al.
    MIT.
    Karumanchi, Sisir B.
    MIT.
    Johnson, Bryan
    Quantum Signal LLC..
    Perlin, Victor
    Quantum Signal LLC..
    Rohde, Mitchell
    Quantum Signal LLC..
    Iagnemma, Karl
    MIT.
    Constraint-based semi-autonomy for unmanned ground vehicles using local sensing2012Inngår i: UNMANNED SYSTEMS TECHNOLOGY XIV, Bellingham, WA: SPIE - International Society for Optical Engineering, 2012, s. Article no. 83870K-Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Teleoperated vehicles are playing an increasingly important role in a variety of military functions. While advantageous in many respects over their manned counterparts, these vehicles also pose unique challenges when it comes to safely avoiding obstacles. Not only must operators cope with difficulties inherent to the manned driving task, but they must also perform many of the same functions with a restricted field of view, limited depth perception, potentially disorienting camera viewpoints, and significant time delays. In this work, a constraint-based method for enhancing operator performance by seamlessly coordinating human and controller commands is presented. This method uses onboard LIDAR sensing to identify environmental hazards, designs a collision-free path homotopy traversing that environment, and coordinates the control commands of a driver and an onboard controller to ensure that the vehicle trajectory remains within a safe homotopy. This system's performance is demonstrated via off-road teleoperation of a Kawasaki Mule in an open field among obstacles. In these tests, the system safely avoids collisions and maintains vehicle stability even in the presence of "routine" operator error, loss of operator attention, and complete loss of communications.

  • 6.
    Anderson, Sterling J.
    et al.
    Massachusetts Institute of Technology.
    Peters, Steven C.
    Massachusetts Institute of Technology.
    Iagnemma, Karl
    Massachusetts Institute of Technology.
    Pilutti, Tom E.
    Ford Research Laboratories, Ford Motor Company, Dearborn, MI, United States.
    A Unified Approach to Semi-Autonomous Control of Passenger Vehicles in Hazard Avoidance Scenarios2009Inngår i: IEEE 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, VOLS 1-9, Piscataway, N.J.: IEEE Press, 2009, s. 2032-2037Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper describes the design of unified active safety framework that combines trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles into a single constrained-optimal-control-based system. This framework allows for multiple actuation modes, diverse trajectory-planning objectives, and varying levels of autonomy. The vehicle navigation problem is formulated as a constrained optimal control problem with constraints bounding a navigable region of the road surface. A model predictive controller iteratively plans the best-case vehicle trajectory through this constrained corridor. The framework then uses this trajectory to assess the threat posed to the vehicle and intervenes in proportion to this threat. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor of travel. Simulated results are presented here to demonstrate the framework's ability to incorporate multiple threat thresholds and configurable intervention laws while sharing control with a human driver. ©2009 IEEE.

  • 7.
    Anderson, Sterling J.
    et al.
    MIT.
    Peters, Steven C.
    MIT.
    Pilutti, Tom E.
    Ford Research Laboratories, Dearborn, MI 48124, United States.
    Iagnemma, Karl
    MIT.
    Design and Development of an Optimal-Control-Based Framework for Trajectory Planning, Threat Assessment, and Semi-autonomous Control of Passenger Vehicles in Hazard Avoidance Scenarios2011Inngår i: Robotics Research, Berlin: Springer Berlin/Heidelberg, 2011, s. 39-54Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper describes the design of an optimal-control-based active safety framework that performs trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios. This framework allows for multiple actuation modes, diverse trajectory-planning objectives, and varying levels of autonomy. A model predictive controller iteratively plans a best-case vehicle trajectory through a navigable corridor as a constrained optimal control problem. The framework then uses this trajectory to assess the threat posed to the vehicle and intervenes in proportion to this threat. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor of travel. Simulation and experimental results are presented here to demonstrate the framework's ability to incorporate configurable intervention laws while sharing control with a human driver. © 2011 Springer-Verlag.

  • 8.
    Anderson, Sterling J.
    et al.
    MIT.
    Peters, Steven C.
    MIT.
    Pilutti, Tom E.
    Ford Research Laboratories, Ford Motor Company, Dearborn, MI, United States.
    Iagnemma, Karl
    MIT.
    Experimental Study of an Optimal-Control-Based Framework for Trajectory Planning, Threat Assessment, and Semi-Autonomous Control of Passenger Vehicles in Hazard Avoidance Scenarios2010Inngår i: FIELD AND SERVICE ROBOTICS, Berlin: Springer Berlin/Heidelberg, 2010, s. 59-68Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper describes the design of an optimal-control-based active safety framework that performs trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios. The vehicle navigation problem is formulated as a constrained optimal control problem with constraints bounding a navigable region of the road surface. A model predictive controller iteratively plans an optimal vehicle trajectory through the constrained corridor. Metrics from this "best-case" scenario establish the minimum threat posed to the vehicle given its current state. Based on this threat assessment, the level of controller intervention required to prevent departure from the navigable corridor is calculated and driver/controller inputs are scaled accordingly. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor of travel. It also allows for multiple actuation modes, diverse trajectory-planning objectives, and varying levels of autonomy. Experimental results are presented here to demonstrate the framework's semi-autonomous performance in hazard avoidance scenarios.

  • 9.
    Arndt, David
    et al.
    L3 Communications, Inc, Pasadena, CA, United States.
    Bobrow, James E.
    Department of Mechanical and Aerospace Engineering, University of California, Irvine, United States.
    Peters, Steven
    MIT, United States.
    Iagnemma, Karl
    MIT, United States.
    Dubowsky, Steven
    MIT, United States.
    Two-Wheel Self-Balancing of a Four-Wheeled Vehicle2011Inngår i: IEEE Control Systems Magazine, ISSN 1066-033X, Vol. 31, nr 2, s. 29-37Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cars and trucks are susceptible to accidents due to rollover. In the United States in 2005, 21.1 of a total of 54,718 deaths in vehicle crashes were caused by rollover [1]. Significant research has therefore been devoted to detecting and preventing rollover through active control. Numerous approaches attempt to detect or predict wheel liftoff using onboard sensing and a combination of automatic steering and braking to keep the wheels on the ground [2][4]. © 2006 IEEE.

  • 10.
    Arvidson, R. E.
    et al.
    Department of Earth and Planetary Sciences, Washington University, St. Louis, MO 63130, United States.
    Ashley, J. W.
    School of Earth and Space Exploration, Mars Space Flight Facility, Arizona State University, Tempe, AZ 85287, United States.
    Bell III, J. F.
    Department of Astronomy, Cornell University, Ithaca, NY 14853, United States.
    Chojnacki, M.
    Planetary Geosciences Institute, Department of Earth and Planetary Sciences, University of Tennessee, Knoxville, TN 37996, United States.
    Cohen, J.
    Honeybee Robotics Spacecraft Mechanisms Corporation, 460 W. 34th St., New York, NY 10001, United States.
    Economou, T. E.
    Laboratory for Astrophysics and Space Research, Enrico Fermi Institute, University of Chicago, Chicago, IL 60637, United States.
    Farrand, W. H.
    Space Science Institute, 4750 Walnut St., Boulder, CO 80301, United States.
    Fergason, R.
    U.S. Geological Survey, 2255 N. Gemini Dr., Flagstaff, AZ 86001, United States.
    Fleischer, I.
    Institut für Anorganische und Analytische Chemie, Johannes Gutenberg-Universität, D-55099 Mainz, Germany.
    Geissler, P.
    U.S. Geological Survey, 2255 N. Gemini Dr., Flagstaff, AZ 86001, United States.
    Gellert, R.
    Department of Physics, University of Guelph, Guelph, ON N1G 2W1, Canada.
    Golombek, M. P.
    Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, United States.
    Grotzinger, J. P.
    Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, United States.
    Guinness, E. A.
    Department of Earth and Planetary Sciences, Washington University, St. Louis, MO 63130, United States.
    Haberle, R. M.
    NASA Ames Research Center, Moffett Field, CA 94035, United States.
    Herkenhoff, K. E.
    U.S. Geological Survey, 2255 N. Gemini Dr., Flagstaff, AZ 86001, United States.
    Herman, J. A.
    Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, United States.
    Iagnemma, Karl
    MIT, USA.
    Jolliff, B. L.
    Department of Earth and Planetary Sciences, Washington University, St. Louis, MO 63130, United States.
    Johnson, J. R.
    U.S. Geological Survey, 2255 N. Gemini Dr., Flagstaff, AZ 86001, United States.
    Klingelhöfer, G.
    Institut für Anorganische und Analytische Chemie, Johannes Gutenberg-Universität, D-55099 Mainz, Germany.
    Knoll, A. H.
    Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, United States.
    Knudson, A. T.
    Planetary Science Institute, 1700 East Fort Lowell, Tucson, AZ 85719, United States.
    Li, R.
    Department of Civil and Environmental Engineering and Geodetic Science, Ohio State University, Columbus, OH 43210, United States.
    McLennan, S. M.
    Department of Geosciences, State University of New York at Stony Brook, Stony Brook, NY 11794, United States.
    Mittlefehldt, D. W.
    NASA Johnson Space Center, Houston, TX 77058, United States.
    Morris, R. V.
    NASA Johnson Space Center, Houston, TX 77058, United States.
    Parker, T. J.
    Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, United States.
    Rice, M. S.
    Department of Astronomy, Cornell University, Ithaca, NY 14853, United States.
    Schröder, C.
    Department of Hydrology, University of Bayreuth, Bayreuth, Germany.
    Soderblom, L. A.
    U.S. Geological Survey, 2255 N. Gemini Dr., Flagstaff, AZ 86001, United States.
    Squyres, S. W.
    Department of Astronomy, Cornell University, Ithaca, NY 14853, United States.
    Sullivan, R. J.
    Department of Astronomy, Cornell University, Ithaca, NY 14853, United States.
    Wolff, M. J.
    Space Science Institute, 4750 Walnut St., Boulder, CO 80301, United States.
    Opportunity Mars Rover mission: Overview and selected results from Purgatory ripple to traverses to Endeavour crater2011Inngår i: Journal of Geophysical Research, ISSN 0148-0227, E-ISSN 2156-2202, Vol. 116, s. E00F15-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Opportunity has been traversing the Meridiani plains since 25 January 2004 (sol 1), acquiring numerous observations of the atmosphere, soils, and rocks. This paper provides an overview of key discoveries between sols 511 and 2300, complementing earlier papers covering results from the initial phases of the mission. Key new results include (1) atmospheric argon measurements that demonstrate the importance of atmospheric transport to and from the winter carbon dioxide polar ice caps; (2) observations showing that aeolian ripples covering the plains were generated by easterly winds during an epoch with enhanced Hadley cell circulation; (3) the discovery and characterization of cobbles and boulders that include iron and stony-iron meteorites and Martian impact ejecta; (4) measurements of wall rock strata within Erebus and Victoria craters that provide compelling evidence of formation by aeolian sand deposition, with local reworking within ephemeral lakes; (5) determination that the stratigraphy exposed in the walls of Victoria and Endurance craters show an enrichment of chlorine and depletion of magnesium and sulfur with increasing depth. This result implies that regional-scale aqueous alteration took place before formation of these craters. Most recently, Opportunity has been traversing toward the ancient Endeavour crater. Orbital data show that clay minerals are exposed on its rim. Hydrated sulfate minerals are exposed in plains rocks adjacent to the rim, unlike the surfaces of plains outcrops observed thus far by Opportunity. With continued mechanical health, Opportunity will reach terrains on and around Endeavour's rim that will be markedly different from anything examined to date. Copyright 2011 by the American Geophysical Union.

  • 11.
    Arvidson, R. E.
    et al.
    Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, United States.
    Bell III, J. F.
    Department of Astronomy, Cornell University, Ithaca, NY, United States.
    Bellutta, P.
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
    Cabrol, N. A.
    NASA Ames Research Center, Moffett Field, CA, United States.
    Catalano, J. G.
    Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, United States.
    Cohen, J.
    Honeybee Robotics Spacecraft Mechanisms Corporation, New York, NY, United States.
    Crumpler, L. S.
    New Mexico Museum of Natural History and Science, Albuquerque, United States.
    Marais, D. J. Des
    NASA Ames Research Center, Moffett Field, CA, United States.
    Estlin, T. A.
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
    Farrand, W. H.
    Space Science Institute, Boulder, CO, United States.
    Gellert, R.
    Department of Physics, University of Guelph, Guelph, ON, Canada.
    Grant, J. A.
    Center for Earth and Planetary Studies, Smithsonian Institution, Washington, DC, United States.
    Greenberger, R. N.
    Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, United States.
    Guinness, E. A.
    Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, United States.
    Herkenhoff, K. E.
    U.S. Geological Survey, Flagstaff, AZ, United States.
    Herman, J. A.
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
    Iagnemma, Karl
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
    Johnson, J. R.
    U.S. Geological Survey, Flagstaff, AZ, United States.
    Klingelhöfer, G.
    Institut für Anorganische und Analytische Chemie, Johannes Gutenberg-Universität, Mainz, Germany.
    Li, R.
    Department of Civil and Environmental Engineering and Geodetic Science, Ohio State University, Columbus, OH, United States.
    Lichtenberg, K. A.
    Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, United States.
    Maxwell, S. A.
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
    Ming, D. W.
    NASA Johnson Space Center, Houston, TX, United States.
    Morris, R. V.
    NASA Johnson Space Center, Houston, TX, United States.
    Rice, M. S.
    Department of Astronomy, Cornell University, Ithaca, NY, United States.
    Ruff, S. W.
    School of Earth and Space Exploration, Arizona State University, Tempe, AZ, United States.
    Shaw, A.
    Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, United States.
    Siebach, K. L.
    Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, United States.
    de Souza, P. A.
    Information and Communication Technologies Centre, CSIRO, Hobart, Australia.
    Stroupe, A. W.
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
    Squyres, S. W.
    Department of Astronomy, Cornell University, Ithaca, NY, United States.
    Sullivan, R. J.
    Department of Astronomy, Cornell University, Ithaca, NY, United States.
    Talley, K. P.
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
    Townsend, J. A.
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
    Wang, A.
    Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, United States.
    Wright, J. R.
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
    Yen, A. S.
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
    Spirit Mars Rover Mission: Overview and selected results from the northern Home Plate Winter Haven to the side of Scamander crater2010Inngår i: Journal of Geophysical Research, ISSN 0148-0227, E-ISSN 2156-2202, Vol. 115, nr 9, s. E00F03-Artikkel i tidsskrift (Fagfellevurdert)
  • 12.
    Arvidson, R.
    et al.
    Washington University in Saint Louis.
    Fuller, D.
    Jet Propulsion Laboratory, Pasadena, CA.
    Heverly, M.
    Jet Propulsion Laboratory, Pasadena, CA.
    Iagnemma, Karl
    MIT.
    Lin, J.
    Jet Propulsion Laboratory, Pasadena, CA.
    Matthews, J.
    SpaceX, Los Angeles, CA.
    Sletten, R.
    5University of Washington.
    Stein, N.
    Washington University in Saint Louis.
    Mars Science Laboratory Curiosity Rover Terramechanics Initial Results2012Konferansepaper (Annet vitenskapelig)
  • 13.
    Arvidson, R.
    et al.
    Washington University in Saint Louis.
    van Dyke, L.
    Washington University in Saint Louis.
    Bennett, K.
    Washington University in Saint Louis.
    Zhou, F.
    Washington University in Saint Louis.
    Iagnemma, Karl
    MIT.
    Senatore, C.
    MIT.
    Lindemann, R.
    Caltech, Pasadena.
    Trease, B.
    Caltech, Pasadena.
    Maxwell, S.
    Caltech, Pasadena.
    Bellutta, P.
    Caltech, Pasadena.
    Stroupe, A.
    Caltech, Pasadena.
    Hartman, F.
    Caltech, Pasadena.
    Verma, V.
    Caltech, Pasadena.
    Ali, K.
    Caltech, Pasadena.
    Mars Exploration Rover Opportunity Terramechanics Across Ripple Covered Bedrock In Meridiani Planum2011Konferansepaper (Annet vitenskapelig)
  • 14.
    Bouguelia, Mohamed-Rafik
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Gonzalez, Ramon
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge, USA.
    Iagnemma, Karl
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge, USA.
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Unsupervised classification of slip events for planetary exploration rovers2017Inngår i: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 73, s. 95-106Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper introduces an unsupervised method for the classification of discrete rovers' slip events based on proprioceptive signals. In particular, the method is able to automatically discover and track various degrees of slip (i.e. low slip, moderate slip, high slip). The proposed method is based on aggregating the data over time, since high level concepts, such as high and low slip, are concepts that are dependent on longer time perspectives. Different features and subsets of the data have been identified leading to a proper clustering, interpreting those clusters as initial models of the prospective concepts. Bayesian tracking has been used in order to continuously improve the parameters of these models, based on the new data. Two real datasets are used to validate the proposed approach in comparison to other known unsupervised and supervised machine learning methods. The first dataset is collected by a single-wheel testbed available at MIT. The second dataset was collected by means of a planetary exploration rover in real off-road conditions. Experiments prove that the proposed method is more accurate (up to 86% of accuracy vs. 80% for K-means) in discovering various levels of slip while being fully unsupervised (no need for hand-labeled data for training). © 2017 ISTVS

  • 15.
    Brooks, C.
    et al.
    MIT.
    Iagnemma, Karl
    MIT.
    Visual Detection of Novel Terrain via Two-Class Classification2009Inngår i: Proceedings of the 24th Annual ACM: Symposium on Applied Computing 2009, New York: ACM Press, 2009, s. 1145-1150Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Remote sensing of terrain characteristics is an important component for autonomous operation of mobile robots in natural terrain. Often this involves classification of terrain into one of a set of a priori known terrain classes. Situations can frequently arise, however, where an autonomous robot encounters a terrain class that does not belong to one of these known classes. This paper proposes an approach for visual detection of novel terrain based on a two-class support vector machine (SVM) for situations when known terrain classes can be confidently associated with only a subset of the training data. Experimental results from a four-wheeled mobile robot in Mars analog terrain demonstrate the effectiveness of this approach. Copyright 2009 ACM.

  • 16.
    Brooks, C.
    et al.
    Field and Space Robotics Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States.
    Iagnemma, Karl
    Field and Space Robotics Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States.
    Dubowsky, S.
    Field and Space Robotics Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States.
    Vibration-based terrain analysis for mobile robots2005Inngår i: Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on, Washington, DC: IEEE Computer Society, 2005, s. 3415-3420Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover's safety on slopes and uneven surfaces. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. Vibrations are measured using an accelerometer on the rover structure. The classifier is trained using labeled vibration data during an off-line learning phase. Linear discriminant analysis is used for on-line identification of terrain classes such as sand, gravel, or clay. This approach is experimentally validated on a laboratory testbed.

  • 17.
    Brooks, C.A.
    et al.
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
    Iagnemma, Karl
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
    Vibration-based terrain classification for planetary exploration rovers2005Inngår i: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 21, nr 6, s. 1185-1191Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover's safety on slopes and uneven surfaces. Visual features are often used to classify terrain; however, vision can be sensitive to lighting variations and other effects. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. This sensing mode is robust to lighting variations. Vibrations are measured using an accelerometer mounted on the rover structure. The classifier is trained using labeled vibration data during an offline learning phase. Linear discriminant analysis is used for online identification of terrain classes, such as sand, gravel, or clay. This approach has been experimentally validated on a laboratory testbed and on a four-wheeled rover in outdoor conditions.

  • 18.
    Brooks, Christopher A.
    et al.
    Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States.
    Iagnemma, Karl
    Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States.
    Self-Supervised Classification for Planetary Rover Terrain Sensing2007Inngår i: Aerospace Conference, 2007 IEEE, Piscataway: IEEE Press, 2007, s. 1-9Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Autonomous mobility in rough terrain is key to enabling increased science data return from planetary rover missions. Current terrain sensing and path planning approaches can be used to avoid geometric hazards, such as rocks and steep slopes, but are unable to remotely identify and avoid non-geometric hazards, such as loose sand in which a rover may become entrenched. This paper proposes a self-supervised classification approach to learning the visual appearance of terrain classes which relies on vibration-based sensing of wheel-terrain interaction to identify these terrain classes. Experimental results from a four-wheeled rover in Mars analog terrain demonstrate the potential for this approach.

  • 19.
    Brooks, Christopher A.
    et al.
    MIT.
    Iagnemma, Karl
    MIT.
    Self-supervised terrain classification for planetary surface exploration rovers2012Inngår i: Journal of Field Robotics, ISSN 1556-4959, Vol. 29, nr 3, s. 445-468Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In future planetary exploration missions, improvements in autonomous rover mobility have the potential to increase scientific data return by providing safe access to geologically interesting sites that lie in rugged terrain, far from landing areas. To improve rover-based terrain sensing, this paper proposes a self-supervised learning framework that will enable a robotic system to learn to predict mechanical properties of distant terrain, based on measurements of mechanical properties of similar terrain that has been traversed previously. In this framework, a proprioceptive terrain classifier is used to distinguish terrain classes based on features derived from rover-terrain interaction, and labels from this classifier are used to train an exteroceptive (i.e., vision-based) terrain classifier. Once trained, the vision-based classifier is able to recognize similar terrain classes in stereo imagery. This paper presents two distinct proprioceptive classifiers-a novel approach based on optimization of a traction force model and a previously described approach based on wheel vibration-as well as a vision-based terrain classification approach suitable for environments with unexpected appearances. The high accuracy of the self-supervised learning framework and its supporting algorithms is demonstrated using experimental data from a four-wheeled robot in an outdoor Mars-analogue environment. © 2012 Wiley Periodicals, Inc.

  • 20.
    Brooks, Christopher A.
    et al.
    Tau Beta Pi, United States.
    Iagnemma, Karl
    MIT, USA.
    Dubowsky, Steven
    MIT, USA.
    Visual wheel sinkage measurement for planetary rover mobility characterization2006Inngår i: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 21, nr 1, s. 55-64Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Wheel sinkage is an important indicator of mobile robot mobility in natural outdoor terrains. This paper presents a vision-based method to measure the sinkage of a rigid robot wheel in rigid or deformable terrain. The method is based on detecting the difference in intensity between the wheel rim and the terrain. The method uses a single grayscale camera and is computationally efficient, making it suitable for systems with limited computational resources such as planetary rovers. Experimental results under various terrain and lighting conditions demonstrate the effectiveness and robustness of the algorithm.

  • 21.
    Buehler, Martin
    et al.
    Boston Dynamics, 515 Massachusetts Avenue, Cambridge, MA 02139, United States.
    Iagnemma, Karl
    MIT, USA.
    Editorial for Journal of Field Robotics - Special issue on the DARPA Grand Challenge2006Inngår i: Journal of Field Robotics, ISSN 1556-4959, Vol. 23, nr 8, s. 461-462Artikkel i tidsskrift (Fagfellevurdert)
  • 22.
    Buehler, Martin
    et al.
    Vecna Technologies, Cambridge, USA.
    Iagnemma, Karl
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
    Singh, Sanjiv
    Carnegie Mellon University, Pittsburgh, USA.
    Editorial: [for the Special issue on the 2007 DARPA Urban Challenge, Part I]2008Inngår i: Journal of Field Robotics, ISSN 1556-4959, Vol. 25, nr 8, s. 423-424Artikkel i tidsskrift (Fagfellevurdert)
  • 23.
    Buehler, Martin
    et al.
    Vecna Technologies, Cambridge, USA.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
    Singh, Sanjiv
    Carnegie Mellon University, Pittsburgh, USA.
    Editorial: [Special issue on the 2007 DARPA Urban Challenge, Part II]2008Inngår i: Journal of Field Robotics, ISSN 1556-4959, Vol. 25, nr 9, s. 567-568Artikkel i tidsskrift (Fagfellevurdert)
  • 24.
    Buehler, Martin
    et al.
    Vecna Technologies, Cambridge, USA.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
    Singh, Sanjiv
    Carnegie Mellon University, Pittsburgh, USA.
    Editorial: [Special Issue on the 2007 DARPA Urban Challenge, Part III]2008Inngår i: Journal of Field Robotics, ISSN 1556-4959, Vol. 25, nr 10, s. 725-726Artikkel i tidsskrift (Fagfellevurdert)
  • 25.
    Cheng, N. G.
    et al.
    MIT.
    Lobovsky, M. B.
    MIT.
    Keating, S. J.
    MIT.
    Setapen, A. M.
    MIT.
    Gero, K. I.
    MIT.
    Hosoi, A. E.
    MIT.
    Iagnemma, Karl
    MIT.
    Design and Analysis of a Robust, Low-cost, Highly Articulated manipulator enabled by jamming of granular media2012Inngår i: Robotics and Automation (ICRA), 2012 IEEE International Conference on, 2012, s. 4328-4333Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Hyper-redundant manipulators can be fragile, expensive, and limited in their flexibility due to the distributed and bulky actuators that are typically used to achieve the precision and degrees of freedom (DOFs) required. Here, a manipulator is proposed that is robust, high-force, low-cost, and highly articulated without employing traditional actuators mounted at the manipulator joints. Rather, local tunable stiffness is coupled with off-board spooler motors and tension cables to achieve complex manipulator configurations. Tunable stiffness is achieved by reversible jamming of granular media, which - by applying a vacuum to enclosed grains - causes the grains to transition between solid-like states and liquid-like ones. Experimental studies were conducted to identify grains with high strength-to-weight performance. A prototype of the manipulator is presented with performance analysis, with emphasis on speed, strength, and articulation. This novel design for a manipulator - and use of jamming for robotic applications in general - could greatly benefit applications such as human-safe robotics and systems in which robots need to exhibit high flexibility to conform to their environments. © 2012 IEEE.

  • 26.
    Cheng, Nadia
    et al.
    MIT, USA.
    Ishigami, Genya
    MIT, USA.
    Hawthorne, Stephan
    MIT, USA.
    Chen, Hao
    University of California, USA.
    Hansen, Malik
    Boston Dynamics, USA.
    Telleria, Maria
    MIT, USA.
    Playter, Robert
    Boston Dynamics, USA.
    Iagnemma, Karl
    MIT, USA.
    Design and Analysis of a Soft Mobile Robot Composed of Multiple Thermally Activated Joints Driven by a Single Actuator2010Inngår i: 2010 IEEE international conference on robotics and automation, Piscataway, N.J.: IEEE Press, 2010, s. 5207-5212Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Soft robotic systems have applications in industrial, medical, and security applications. Many applications require these robots to be small and lightweight. One challenge in developing a soft robotic system is to drive multiple degrees-of-freedom (DOF) with few actuators, thereby reducing system size and weight. This paper presents the analysis and design of an inchworm-like mobile robot that consists of multiple, independent thermally activated joints but is driven by a single actuator. To realize control of this under-actuated system, a solder-based locking mechanism has been developed to selectively activate individual joints without requiring additional actuators. The design and performance analysis of a prototype mobile robot that is capable of inchworm-like translational and steering motion is described. The design of novel "feet" with anisotropic friction properties is also described. ©2010 IEEE.

  • 27.
    David, Jennifer
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Valencia, Rafael
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Task Assignment and Trajectory Planning in Dynamic environments for Multiple Vehicles2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We consider the problem of finding collision-free trajectories for a fleet of automated guided vehicles (AGVs) working in ship ports and freight terminals. Our solution computes collision-free trajectories for a fleet of AGVs to pick up one or more containers and transport it to a given goal without colliding with other AGVs and obstacles. We propose an integrated framework for solving the goal assignment and trajectory planning problem minimizing the maximum cost overall vehicle trajectories using the classical Hungarian algorithm.To deal with the dynamics in the environment, we refine our final trajectories with CHOMP (Covariant Hamiltonianoptimization for motion planning) in order to trade off between path smoothness and dynamic obstacle avoidance.

  • 28.
    David, Jennifer
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Valencia, Rafael
    Carnegie Mellon University, Pittsburgh, USA.
    Philippsen, Roland
    Google Inc..
    Bosshard, Pascal
    ETH Zürich, Zürich, Switzerland.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Gradient Based Path Optimization Method for Autonomous Driving2017Inngår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), [Piscataway, NJ]: IEEE, 2017, s. 4501-4508Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper discusses the possibilities of extending and adapting the CHOMP motion planner to work with a non-holonomic vehicle such as an autonomous truck with a single trailer. A detailed study has been done to find out the different ways of implementing these constraints on the motion planner. CHOMP, which is a successful motion planner for articulated robots produces very fast and collision-free trajectories. This nature is important for a local path adaptor in a multi-vehicle path planning for resolving path-conflicts in a very fast manner and hence, CHOMP was adapted. Secondly, this paper also details the experimental integration of the modified CHOMP with the sensor fusion and control system of an autonomous Volvo FH-16 truck. Integration experiments were conducted in a real-time environment with the developed autonomous truck. Finally, additional simulations were also conducted to compare the performance of the different approaches developed to study the feasibility of employing CHOMP to autonomous vehicles. ©2017 IEEE

  • 29.
    David, Jennifer
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Valencia, Rafael
    Carnegie Mellon University, Pittsburgh, USA.
    Philippsen, Roland
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, USA.
    Local Path Optimizer for an Autonomous Truck in a Harbour Scenario2017Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Recently, functional gradient algorithms like CHOMP have been very successful in producing locally optimal motion plans for articulated robots. In this paper, we have adapted CHOMP to work with a non-holonomic vehicle such as an autonomous truck with a single trailer and a differential drive robot. An extended CHOMP with rolling constraints have been implemented on both of these setup which yielded feasible curvatures. This paper details the experimental integration of the extended CHOMP motion planner with the sensor fusion and control system of an autonomous Volvo FH-16 truck. It also explains the experiments conducted on the differential-drive robot. Initial experimental investigations and results conducted in a real-world environment show that CHOMP can produce smooth and collision-free trajectories for mobile robots and vehicles as well. In conclusion, this paper discusses the feasibility of employing CHOMP to mobile robots.

  • 30.
    Dimopoulos, A.
    et al.
    Natl Tech Univ Athens, Dept Mech Engn, Athens 15780, Greece.
    Papadopoulos, E.
    Natl Tech Univ Athens, Dept Mech Engn, Athens 15780, Greece.
    Iagnemma, Karl
    MIT, USA.
    Experimental tissue parameter identification for use in endoscopic urological haptic simulators2009Inngår i: Control and Automation, 2009. MED ’09. 17th Mediterranean Conference on, Piscataway, N.J.: IEEE Press, 2009, s. 1131-1136Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A method is developed for obtaining parameters for tube-like soft-tissues, to be used in urological haptic simulators. A device was designed and built that allows the acquisition of forces and displacements during endoscope insertion in a tubelike soft tissue. The device consists of a mechatronic ball screw mechanism, with a 6 DOF force/ torque sensor attached to it. The steel shaft representing the endoscope, is commanded to follow desired trajectories with micrometric accuracy under the application of an IPD controller, implemented on a dSpace 1103 system. The experimental data acquired is fitted to polynomials to yield a tissue model that can be used to predict insertion forces required for haptic simulator feedback.

  • 31.
    Dubowsky, S.
    et al.
    MIT, USA.
    Iagnemma, Karl
    MIT, USA.
    Liberatore, S.
    MIT, USA.
    Lambeth, D. M.
    MIT, USA.
    Plante, J. S.
    MIT, USA.
    Boston, P. J.
    New Mexico Tech. NM Institute of Mining and Technology, Socorro, NM 87801, United States.
    A concept mission: Microbots for large-scale planetary surface and subsurface exploration2005Inngår i: Space technology and applications international forum: STAIF 2005 / [ed] Mohamed S El-Genk, College Park, MD: American Institute of Physics (AIP), 2005, s. 1449-1458Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a new mission concept for planetary exploration, based on the deployment of a large number of small spherical mobile robots ("microbots") over vast areas of a planet's surface and subsurface, including structures such as caves and near-surface crevasses (see Figure 1). This would allow extremely large-scale in situ analysis of terrain composition and history. This approach represents an alternative to rover and lander-based planetary exploration, which is limited to studying small areas of a planet's surface at a small number of sites. The proposed approach is also distinct from balloon or aerial vehicle-based missions, in that it would allow direct in situ measurement. In the proposed mission, a large number (i.e. hundreds or thousands) of cm-scale, sub-kilogram microbots would be distributed over a planet's surface by an orbital craft and would employ hopping, bouncing and rolling as a locomotion mode to reach scientifically interesting artifacts in very rugged terrain. They would be powered by high energy-density polymer "muscle" actuators, and equipped with a suite of miniaturized imagers, spectrometers, sampling devices, and chemical detection sensors to conduct in situ measurements of terrain and rock composition, structure, etc. Multiple microbots would coordinate to share information, cooperatively analyze large portions of a planet's surface or subsurface, and provide context for scientific measurements. © 2005 American Institute of Physics.

  • 32.
    Gholami Shahbandi, Saeed
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Magnusson, Martin
    Örebro University, Örebro, Sweden .
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer2018Inngår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, nr 3, s. 2040-2047Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We propose a method based on a nonlinear transformation for nonrigid alignment of maps of different modalities, exemplified with matching partial and deformed two-dimensional maps to layout maps. For two types of indoor environments, over a dataset of 40 maps, we have compared the method to state-of-the-art map matching and nonrigid image registration methods and demonstrate a success rate of 80.41% and a mean point-to-point alignment error of 1.78 m, compared to 31.9% and 10.7 m for the best alternative method. We also propose a fitness measure that can quite reliably detect bad alignments. Finally, we show a use case of transferring prior knowledge (labels/segmentation), demonstrating that map segmentation is more consistent when transferred from an aligned layout map than when operating directly on partial maps (95.97% vs. 81.56%). © 2018 IEEE.

  • 33.
    Gonzalez, Ramon
    et al.
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers2016Inngår i: Proceedings of the 8th ISTVS Americas Conference, Detroit, September 12-14, 2016., 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper analyzes the advantages and limitations of known machine learning approaches to cope with the problem of incipient rover embedding detection based on propioceptive signals. In particular, two supervised learning approaches (Support Vector Machines and Feed-forward Neural Networks) are compared to two unsupervised learning approaches (K-means and Self-Organizing Maps) in order to identify various degrees of slip (e.g. low slip, moderate slip, high slip). A real dataset collected by a single-wheel testbed available at MIT has been used to validate each strategy. The SVM algorithm achieves the best performance (accuracy >95 %). However, the SOM algorithm represents a better solution in terms of accuracy and the need of hand-labeled data for training the classifier (accuracy >84 %).

  • 34.
    Halatci, Ibrahim
    et al.
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, United States.
    Brooks, Christopher A.
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, United States.
    Iagnemma, Karl
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, United States.
    A study of visual and tactile terrain classification and classifier fusion for planetary exploration rovers2008Inngår i: Robotica (Cambridge. Print), ISSN 0263-5747, E-ISSN 1469-8668, Vol. 26, nr 6, s. 767-779Artikkel i tidsskrift (Fagfellevurdert)
  • 35.
    Halatci, Ibrahim
    et al.
    Massachusetts Institute of Technology, Department of Mechanical Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, United States.
    Brooks, Christopher A.
    Massachusetts Institute of Technology, Department of Mechanical Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, United States.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Department of Mechanical Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, United States.
    Terrain Classification and Classifier Fusion for Planetary Exploration Rovers2007Inngår i: Aerospace Conference, 2007 IEEE, Piscataway: IEEE Press, 2007, s. 1-11Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Knowledge of the physical properties of terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Here a study of multi-sensor terrain classification for planetary rovers in Mars and Mars-like environments is presented. Two classification algorithms for color, texture, and range features are presented based on maximum likelihood estimation and support vector machines. In addition, a classification method based on vibration features derived from rover wheel-terrain interaction is briefly described. Two techniques for merging the results of these "low-level" classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performance of these algorithms is studied using images from NASA's Mars Exploration Rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. It is shown that accurate terrain classification can be achieved via classifier fusion from visual and tactile features.

  • 36.
    Helmick, D.
    et al.
    Jet Propulsion Laboratory.
    Angelova, A.
    Jet Propulsion Laboratory.
    Matthies, L.
    Jet Propulsion Laboratory.
    Brooks, C.
    MIT.
    Halatci, I.
    MIT.
    Dubowsky, S.
    MIT.
    Iagnemma, Karl
    MIT.
    Experimental Results from a Terrain Adaptive Navigation System for Planetary Rovers2008Inngår i: Proceedings of the Ninth International Symposium on Artificial Intelligence, Robotics and Automation in Space, i-SAIRAS, 2008Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Results from the experimental testing of a navigation system for planetary rovers called Terrain Adaptive Navigation (TANav) are shown here. This system was designed to enable greater access to and more robustoperations in terrains with widely varying slippage.The system achieves this goal by using onboard stereocameras to remotely classify terrain, predict the slippage of that terrain, and use this information in the planning of a path to the goal. An end-to-end onboard demonstration of the system in a Mars analog environment is shown with promising results.

  • 37.
    Howard, A.
    et al.
    Space Exploration Technologies Corporation (SpaceX), CA, Unites States.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
    Kelly, A.
    Carnegie Mellon University, Unites States.
    Seventh International Conference on Field and Service Robotics2010Inngår i: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 29, nr 12, s. 1459-1460Artikkel i tidsskrift (Fagfellevurdert)
  • 38.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
    Terrain Estimation Methods for Enhanced Autonomous Rover Mobility2006Inngår i: Intelligence for Space Robotics / [ed] Ayanna M. Howard & Edward W. Tunstel, San Antonio, Tex.: TSI Press , 2006Kapittel i bok, del av antologi (Fagfellevurdert)
  • 39.
    Iagnemma, Karl
    et al.
    Boston Dynamics, Boston, MA, United States.
    Buehler, M.
    Boston Dynamics, Boston, MA, United States.
    Editorial for Journal of Field Robotics: Special issue on the DARPA Grand Challenge2006Inngår i: Journal of Field Robotics, ISSN 1556-4959, Vol. 23, nr 9, s. 655-656Artikkel i tidsskrift (Fagfellevurdert)
  • 40.
    Iagnemma, Karl
    et al.
    Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
    Martinet, Philippe
    Blaise Pascal University, Clermont-Ferrand, France.
    Wang, Danwei
    Nanyang Technological University, Singapore.
    Introduction: Vehicle–Terrain Interaction for Mobile Robots2010Inngår i: Journal of Field Robotics, ISSN 1556-4959, Vol. 27, nr 2, s. 105-106Artikkel i tidsskrift (Fagfellevurdert)
  • 41.
    Iagnemma, Karl
    et al.
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
    Peters, Steven
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
    Stability Measurement of High Speed Vehicles on Sloped and Rough Terrain2009Inngår i: Vehicle System Dynamics, ISSN 0042-3114, E-ISSN 1744-5159, Vol. 47, nr 6, s. 701-720Artikkel i tidsskrift (Fagfellevurdert)
  • 42.
    Iagnemma, Karl
    et al.
    MIT.
    Senatore, C.
    MIT.
    Trease, B.
    Caltech.
    Arvidson, R.
    MIT.
    Bennett, K.
    MIT.
    Shaw, A.
    MIT.
    Zhou, F.
    MIT.
    Van Dyke, L.
    MIT.
    Lindemann, R.
    Caltech.
    Terramechanics Modeling of Mars Surface Exploration Rovers for Simulation and Parameter Estimation2011Inngår i: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference--2011: : presented at ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 28-31, 2011, Washington, D.C., New York: ASME Press, 2011, s. 805-812Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In 1997 and 2004, small wheeled robots ("rovers") landed on the surface of Mars to conduct scientific experiments focused on understanding the planet's climate history, surface geology, and potential for past or present life. Recently, the Mars Exploration Rover(MER)"Spirit"became deeply embedded in regolith at a site called Troy, ending its mission as a mobile science platform. The difficultyfaced in navigating mobile robots over sloped, rocky, and deformable terrain has highlighted the importance of developing accurate simulation tools for use in a predictive mobility modeling capacity. These simulation tools require accurate knowledge of terrain model parameters.This paperdescribes a terramechanics-based toolfor simulation of rover mobility. It also describes ongoing work toward estimation of terrain parameters of Mars soil. Copyright © 2011 by ASME.

  • 43.
    Iagnemma, Karl
    et al.
    Massachusetts Institute of Technology, Cambridge, MA, United States.
    Shibly, Hassan
    Birzeit University, Birzeit, Palestinian Territories.
    Dubowsky, Steven
    Massachusetts Institute of Technology, Cambridge, MA, United States.
    On-Line Terrain Parameter Estimation for Planetary Rovers2002Inngår i: Proceedings: 2002 IEEE International Conference on Robotics and Automation, May 11–15, Washington D.C., Piscataway, NJ: IEEE Press, 2002, Vol. 3, s. 3142-3147Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Future planetary exploration missions will require rovers to traverse very rough terrain with limited human supervision. Wheel-terrain interaction plays a critical role in rough-terrain mobility. In this paper an on-line estimation method that identifies key terrain parameters using on-board rover sensors is presented. These parameters can be used for accurate traversability prediction or in a traction control algorithm. These parameters are also valuable indicators of planetary surface soil composition. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for loose sand.

  • 44.
    Iagnemma, Karl
    et al.
    MIT.
    Shimoda, Shingo
    RIKEN BSI, Japan.
    Shiller, Zvi
    Ariel University Center, Israel.
    Near-Optimal Navigation of High Speed Mobile Robots on Uneven Terrain2008Inngår i: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Vols 1-3, Piscataway, N.J.: IEEE Press, 2008, s. 4098-4103Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper proposes a method for near-optimal navigation of high speed mobile robots on uneven terrain. The method relies on a layered control strategy. A high-level planning layer generates an optimal desired trajectory through uneven terrain. A low-level navigation layer guides a robot along the desired trajectory via a potential field-based control algorithm. The high-level planner is guaranteed to yield optimal trajectories but is computationally intensive. The low-level navigation layer is sub-optimal but computationally efficient. To guard against failures at the navigation layer, a model-based lookahead approach is employed that utilizes a reduced form of the optimal trajectory generation algorithm. Simulation results show that the proposed method can successfully navigate a mobile robot over uneven terrain while avoiding hazards. A comparison of the method's performance to a similar algorithm is also presented. ©2008 IEEE.

  • 45.
    Iagnemma, Karl
    et al.
    MIT.
    Udengaard, M.
    MIT.
    Ishigami, G.
    MIT.
    Spenko, M.
    Illinois Institute of Technology.
    Oncu, S.
    Illinois Institute of Technology.
    Khan, I.
    Illinois Institute of Technology.
    Overholt, J.
    U.S. Army Tank Automotive Research Development and Engineering Center (TARDEC).
    Hudas, G.
    U.S. Army Tank Automotive Research Development and Engineering Center (TARDEC).
    Design and Development of an Agile, Man Portable Unmanned Ground Vehicle2008Inngår i: Proceedings of the 26th Annual Army Science Conference, 2008, s. 1-8Konferansepaper (Fagfellevurdert)
    Abstract [en]

    An omnidirectional unmanned ground vehicle (UGV)is able to move in any planar direction regardless of itscurrent pose. To date, nearly all designs and analyses ofomnidirectional robots have considered the case ofmotion on flat, smooth terrain. This paper presents thedesign, analysis, and prototype development of a manportable omnidirectional UGV designed for operation inrough terrain. Design guidelines are presented that arederived from geometric constraints on wheel and linkagesizes. The effects of terrain roughness and loss of wheelcontact on UGV mobility are also analyzed.Aframework for UGV design optimization is presented thatconsiders vehicle kinematic isotropy, wheel-terraininteraction properties, predicted obstacle traversability,and maximum traversable distance over various outdoorterrain types. The results are used to design two small(i.e. 1m characteristic length), lightweight (i.e.approximately 25 kg) UGV prototypes.

  • 46.
    Iagnemma, Karl
    et al.
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, USA.
    Ward, Chris C.
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, USA.
    Classification-based wheel slip detection and detector fusion for mobile robots on outdoor terrain2009Inngår i: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 26, nr 1, s. 33-46Artikkel i tidsskrift (Fagfellevurdert)
  • 47.
    Ishigami, G.
    et al.
    Japan Aerospace Exploration Agency, Sagamihara, Japan.
    Otsuki, M.
    Japan Aerospace Exploration Agency, Sagamihara, Japan.
    Kubota, T.
    Japan Aerospace Exploration Agency, Sagamihara, Japan.
    Iagnemma, Karl
    MIT.
    Modeling of Flexible and Rigid Wheels for Exploration Rover on Rough Terrain2011Inngår i: Proceedings of the 28th International Symposium on Space Technology and Science, 2011Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a comprehensive wheel model that can quantitatively evaluate traction performance of flexible/rigid wheelsdriving on deformable terrain. The proposed model exploits a terramechanics-based approach with taking account of pressures generated by wheel elasticity as well as terrain stiffness. Deflection of a flexible wheel typically depends on a relative pressure between thewheel and terrain: the wheel will be significantly deformed on rigid terrain whereas it will be hardly deformed on soft terrain. Therefore, the wheel-terrain interaction in the proposed model is divided into three contact sections: wheel front section, wheel deflected(flat) section, and wheel rear section. The traction force of the wheel is obtained as an integral of normal and shear stresses generatedat each section. Simulation studies with varied wheel pressures, such as flexible, semi-flexible, and rigid wheels, are conducted tovalidate the proposed model. Also, traction performances of flexible/rigid wheels are compared based on a metric called tractiveefficiency. The comparison implies an optimal wheel pressure of flexible wheel for better traction performance.

  • 48.
    Ishigami, G.
    et al.
    Japan Aerospace Exploration Agency.
    Overholt, J.
    TARDEC.
    Iagnemma, Karl
    MIT.
    Multi-material Anisotropic Friction Wheels for Omnidirectional Ground Vehicles2010Inngår i: Proceedings of the ICAM 5th International Conference on Advanced Mechatronics, 2010, s. 658-662Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, a novel omnidirectional vehicle with anisotropic friction wheels is presented. The proposed wheel has a series of bendable “nodes” on its circumference, each of which is made of two materials with differing friction properties: one material exhibits high friction, and the other exhibits low friction. The high friction section of the node generates a high traction force, while the low friction section enables the wheel to passively slide. The wheels are arranged such that the robot wheel exhibits high traction in its driving direction (much like a conventional tire), but low traction when sliding laterally. Due to this “anisotropic friction” property, the proposed wheel enables a vehicle to realize omnidirectional motion (i.e. the vehicle can move any direction within the plane—forward, back, or laterally). While many other omnidirectional wheel drives exist, the proposed wheel is simpler than any other existing design because the wheel is composed of a single, moldable element. This paper summarizes the design of the proposed wheel and presents a comparison between a small omnidirectional vehicle that uses the proposed wheel and an omnidirectional vehicle that uses conventional wheels.

  • 49.
    Ishigami, G.
    et al.
    Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, 3-1-1 Yoshinodai, Sagamihara, 252-5210, Japan.
    Pineda, E.
    MIT.
    Overholt, J.
    U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC), Warren, MI 48397, United States.
    Hudas, G.
    U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC), Warren, MI 48397, United States.
    Iagnemma, Karl
    MIT.
    Performance analysis and odometry improvement of an omnidirectional mobile robot for outdoor terrain2011Inngår i: Proceedings: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems : September 25-30, 2011, San Francisco, California, USA, Piscataway, N.J.: IEEE Press, 2011, s. 4091-4096Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper an omnidirectional mobile robot that possesses high mobility in rough terrain is presented. The omnidirectional robot has four active split offset caster (ASOC) modules, enabling the robot to move in any planar direction. It also possesses passive suspension articulation, allowing the robot to conform to uneven terrain. The agility of the robot is experimentally evaluated in various configurations. In addition, an odometry method that mitigates position estimation error due to wheel slippage is proposed. A key aspect of the proposed method is to utilize sensory data of wheel velocity, and turning rate around each ASOC pivot shaft, along with kinematic constraints of the robot configuration. Experimental odometry tests with different maneuvers in rough terrain are presented that confirm the utility of the proposed method. © 2011 IEEE.

  • 50.
    Ishigami, Genya
    et al.
    Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
    Kewlani, Gaurav
    Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
    Predictable Mobility: A Statistical Approach for Planetary Surface Exploration Rovers in Uncertain Terrain2009Inngår i: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 16, nr 4, s. 61-70Artikkel i tidsskrift (Fagfellevurdert)
123 1 - 50 of 106
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