Gregg Podnar

Carnegie Mellon University, Pittsburgh, PA, United States

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Publications (16)2.57 Total impact

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    ABSTRACT: We describe the design and performance of a hand-held actively stabilized tool to increase accuracy in micro-surgery or other precision manipulation. It removes involuntary motion such as tremor by actuating the tip to counteract the effect of the undesired handle motion. The key components are a three-degree-of-freedom piezoelectric manipulator that has 400 μm range of motion, 1 N force capability, and bandwidth over 100 Hz, and an optical position measurement subsystem that acquires the tool pose with 4 μm resolution at 2000 samples/s. A control system using these components attenuates hand motion by at least 15 dB (a fivefold reduction). By considering the effect of the frequency response of Micron on the human visual feedback loop, we have developed a filter that reduces unintentional motion, yet preserves intuitive eye-hand coordination. We evaluated the effectiveness of Micron by measuring the accuracy of the human/machine system in three simple manipulation tasks. Handheld testing by three eye surgeons and three non-surgeons showed a reduction in position error of between 32% and 52%, depending on the error metric.
    IEEE Transactions on Robotics 02/2012; 28(1):195-212. · 2.57 Impact Factor
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    ABSTRACT: The successful introduction of electric vehicles continues to be stifled by the high cost and limited performance life of battery technology. We assert that a disruptive improvement in systems-level cost-of-performance is possible by employing a rate-heterogeneous energy storage system, combining low-rate batteries and high-rate supercapacitors, that is mated to a predictive control system that optimizes power management by exploiting topographic information, traffic history, and specific driver performance. Such predictive power management, optimizing energy storage throughout episodes of vehicle acceleration and regenerative braking, has the potential to significantly decrease the total energy duty on the vehicle's batteries.
    Integrated and Sustainable Transportation System (FISTS), 2011 IEEE Forum on; 08/2011
  • G. Podnar, J. Dolan, A. Elfes
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    ABSTRACT: We present a telesupervision approach for supporting environmental science sensors deployed on a fleet of networked autonomous surface vessels to enable in situ study of oceans, lakes, and rivers. This architecture increases data-gathering effectiveness and science return while reducing demands on scientists by using a multi-level autonomy control architecture, where the operating mode of the vehicles ranges from autonomous control to teleoperated human control. Results are presented of real-world deployments of both ocean-going robotic sensor platforms in the Chincoteague Bay, and surface water sensor boats in a small freshwater lake. The target field applications of the technology are the characterization of Harmful Algal Blooms (HABs), and water quality. The field tests conducted include: controlled tests to prove the system functionality using rhodamine dye as a HAB simulant; cooperative ocean sensing of ocean chlorophyll-a; and lake water quality mapping data. We discuss the cooperative operational issues, and the end-to-end networked communications issues for the telesupervision of these heterogeneous and geographically widely-dispersed robotic sensor platforms. Aerial image of ocean-going robotic sensor platform about to traverse fluorescent dye simulating a harmful algal bloom. Three Robot Sensor Boats autonomously measuring water quality of a small lake.
    AGU Fall Meeting Abstracts. 12/2010;
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    ABSTRACT: We present a fleet of autonomous Robot Sensor Boats (RSBs) developed for lake and river fresh water quality assessment and controlled by our Multilevel Autonomy Robot Telesupervision Architecture (MARTA). The RSBs are low cost, highly maneuverable, shallow draft sensor boats, developed as part of the Sensor Web program supported under the Advanced Information Systems Technology program of NASA's Earth Systems Technology Office. They can scan large areas of lakes, and navigate up tributaries to measure water quality near outfalls that larger research vessels cannot reach. The MARTA telesupervision architecture has been applied to a number of domains from multi-platform autonomous wide area planetary mineral prospecting, to multi-platform ocean monitoring. The RSBs are a complementary expansion of a fleet of NOAA/NASA-developed extended-deployment surface autonomous vehicles that enable in-situ study of meteorological factors of the ocean/atmosphere interface, and which have been adapted to investigate harmful algal blooms under this program. The flexibility of the MARTA telesupervision architecture was proven as it supported simultaneous operation of these heterogenous autonomous sensor platforms while geographically widely separated. Results and analysis are presented of multiple tests carried out over three months using a multi-sensor water sonde to assess water quality in a small recreational lake. Inference Grids were used to produce maps representing temperature, pH, and dissolved oxygen. The tests were performed under various water conditions (clear vs. hair algae-laden) and both before and after heavy rains. Data from each RSB was relayed to a data server in our lab in Pittsburgh, Pennsylvania, and made available over the World Wide Web where it was acquired by team members at the Jet Propulsion Laboratory of NASA in Pasadena, California who monitored the boats and their sensor readings in real time, as well as using these data to model the water quality by p- - roducing Inference Grid-based maps.
    Aerospace Conference, 2010 IEEE; 04/2010
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    ABSTRACT: Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth's climate and ecology. Typical ocean sensing is done with satellites or in situ buoys and research ships which are slow to reposition. Cloud cover inhibits study of localized transient phenomena such as Harmful Algal Blooms (HAB). A fleet of extended-deployment surface autonomous vehicles will enable in situ study of characteristics of HAB, coastal pollutants, and related phenomena. We have developed a multiplatform telesupervision architecture that supports adaptive reconfiguration based on environmental sensor inputs. Our system allows the autonomous repositioning of smart sensors for HAB study by networking a fleet of NOAA OASIS (Ocean Atmosphere Sensor Integration System) surface autonomous vehicles. In situ measurements intelligently modify the search for areas of high concentration. Inference Grid and complementary information-theoretic techniques support sensor fusion and analysis. Telesupervision supports sliding autonomy from high-level mission tasking, through vehicle and data monitoring, to teleoperation when direct human interaction is appropriate. This paper reports on experimental results from multi-platform tests conducted in the Chesapeake Bay and in Pittsburgh, Pennsylvania waters using OASIS platforms, autonomous kayaks, and multiple simulated platforms to conduct cooperative sensing of chlorophyll-a and water quality.
    Proc SPIE 09/2009;
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    ABSTRACT: This paper describes the Multilevel Autonomy Robot Telesupervision Architecture (MARTA), an architecture for supervisory control of a heterogeneous fleet of net-worked unmanned autonomous aquatic surface vessels carrying a payload of environmental science sensors. This architecture allows a land-based human scientist to effectively supervise data gathering by multiple robotic assets that implement a web of widely dispersed mobile sensors for in situ study of physical, chemical or bio-logical processes in water or in the water/atmosphere interface.
    01/2009;
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    ABSTRACT: This paper describes a multi-robot science exploration software architecture and system called the telesupervised adaptive ocean sensor fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended-deployment autonomous ocean surface vessels, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a multi-level autonomy control architecture, where the operating mode of the vehicles ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.
    Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on; 06/2008
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    ABSTRACT: Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth's climate and ecology. Ocean sensing is typically done with satellites, buoys, and crewed research ships. The limitations of these systems include the fact that satellites are often blocked by cloud cover, and buoys and ships have spatial coverage limitations. This paper describes a Multilevel Autonomy Robot Telesupervision Architecture (MARTA) for multi-robot science exploration, and an embodiment of the MARTA architecture in a real-world system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in-situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended- deployment autonomous ocean surface vehicles, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using the MARTA multi-level autonomy control architecture, where the operating mode of the vessels ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF system and the underlying MARTA architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.
    Aerospace Conference, 2008 IEEE; 04/2008
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    ABSTRACT: Mobile sensing platforms provide a new modality for exploring the natural world. Robotic vehicles can be quickly deployed to new areas of interest and can have their sensor payload configured to the specific natural and environmental processes to be investigated. Building integrated sensing architectures that coordinate the operation of stationary networks and mobile platforms will allow researchers to take advantage of the strengths of both modalities, opening up new opportunities for scientific research and environmental monitoring. Among the various challenges to be faced, there are two key interrelated issues that are common to autonomous mobile platforms: the representation and modeling of natural processes using the sensor data being collected, and the use of this information to provide guidance, navigation and control for the mobile platforms. Both are addressed using a stochastic lattice-based framework for robot mapping, planning and control called the Inference Grid. In this paper, we will review our work on environmental robotic platforms, discuss how Inference Grids are used for natural process representation as well as for planning and control of autonomous robot vehicles, and show selected experimental results from field tests.
    01/2008;
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    ABSTRACT: We are developing a multi-robot science exploration architecture and system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF uses a group of robotic boats (the OASIS platforms) to enable in-situ study of ocean surface and sub-surface phenomena. The OASIS boats are extended-deployment autonomous ocean surface vehicles, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. It allows multiple mobile sensing assets to function in a cooperative fashion, and the operating mode of the vessels to range from autonomous control to teleoperated control. In this manner, TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for tasking, control, and monitoring. It combines and extends prior related work done by the authors and their institutions. The TAOSF architecture is applicable to other areas where multiple sensing assets are needed, including ecological forecasting, water management, carbon management, disaster management, coastal management, homeland security, and planetary exploration. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). Several components of the TAOSF system have been tested, including the OASIS boats, the communications and control interfaces between the various hardware and software subsystems, and an airborne sensor validation system. Field tests in support of future HAB characterization were performed under controlled conditions, using rhodamine dye as a HAB simulant that was dispersed in a pond. In this paper, we describe the overall TAOSF architecture and its components, discuss the initial tests conducted and outline the next steps.
    Proc SPIE 09/2007;
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    ABSTRACT: Lunar and planetary surfaces are the most hostile working environments into which humans can be sent. The protective spacesuit is massive and cumbersome, with EVA mission time limited by both the suit's resources and the astronaut's stamina. To maintain human presence on the Moon and to expand it to Mars requires enormous investments in transportation and life support for each human. Therefore, successful and sustainable space exploration and operations must maximize the efficiency of every astronaut and keep them "as safe as reasonably achievable". Towards this goal, tasks for which current robotic autonomy technologies are effective should be offloaded from the astronauts. However, whenever the limits of autonomy are reached, a human will need to intervene, preferably by telesupervising the robotic assets (thus reducing EVAs). Employing an effective telesupervision architecture to augment the ingenuity of a human supervisor with state-of-the-art autonomous systems results in a manifold increase in the human's performance and a significant improvement in safety. This completely changes the risk profile of a mission, and allows astronauts to perform substantial amounts of hazardous work from a well-supplied operations base, such as an orbital station, a CEV, or a Lunar or Martian habitat. Telesupervised robotic systems have been identified as a key technology by the NASA Exploration Systems Mission Directorate, and are crucial to the success of the Vision for Space Exploration. However, very little applicable work has been done in the design of telesupervised system architectures, the appropriate mix of autonomy and remote control, and context switching between them, or in the testing and deployment of such systems. This paper focuses on the development of an advanced telesupervision system architecture that will provide a highly efficient approach to human-robot interaction while allowing very heterogeneous robotic assets to be deployed. These assets include exploration rovers and climbers; large autonomous miners and transporters; stationary ISRU processing plants, materials fabricators, and power stations; and construction and maintenance robots. We argue that for the telesupervisor to acquire the state of each varied robot and its environment involves not only telemetry and high-fidelity telepresence (including proprioceptive cues), but also a sensorial "playback" of the recent history of autonomous operation that will reveal the issues that led to the crisis that now requires assistance. Providing the framework within which this history and context are acquired and reproduced is crucial to a viable telesupervision architecture.
    01/2007;
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    ABSTRACT: Current NASA plans envision human beings returning to the Moon in 2018 and, once there, establishing a permanent outpost from which we may initiate a long-term effort to visit other planetary bodies in the Solar System. This will be a bold, risky, and costly journey, comparable to the Great Navigations of the fifteenth and sixteenth centuries. Therefore, it is important that all possible actions be taken to maximize the astronauts' safety and productivity. This can be achieved by deploying fleets of autonomous robots for mineral prospecting and mining, habitat construction, fuel production, inspection and maintenance, etc.; and by providing the humans with the capability to telesupervise the robots' operation and to teleoperate them whenever necessary or appropriate, all from a safe, "shirtsleeve" environment. This paper describes the authors' work in progress on the development of a Robot Supervision Architecture (RSA) for safe and efficient space exploration and operation. By combining the humans' advanced reasoning capabilities with the robots' suitability for harsh space environments, we will demonstrate significant productivity gains while reducing the amount of weight that must be lifted from Earth – and, therefore, cost.
    01/2006;
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    ABSTRACT: A successful plan for space exploration requires the commissioning of fleets of robots to prospect, mine, build, inspect and maintain structures, and generally assist astronauts, rendering the overall mission as safe as reasonably achievable for human beings, the most precious resource. The authors are currently developing, under the support of NASA, a Robot Supervision Architecture (RSA) which will allow a small number of human operators to safely and efficiently telesupervise a fleet of autonomous robots. This represents a significant advance over the state of the art, where currently one robot is overseen by a group of skilled professionals. In this paper we describe some aspects of this work, including the architecture itself for coordination of human and robot work, failure and contingency management, high-fidelity telepresence, and operation under limited bandwidth. We also present highlights of our first application: wide area prospecting of minerals and water in support of sustained outposts on the Moon and on Mars. NASA has initiated the implementation of its Vision for Space Exploration by planning to return human beings to the Moon by 2018 and then proceed to Mars by 2030. This bold, risky, and costly enterprise will require that all possible actions be taken to maximize the astronauts' safety and efficiency. The authors believe that this can be facilitated by fleets of robots autonomously performing a wide variety of tasks such as in-space inspection, maintenance and assembly; regional surveys, mineral prospecting and mining; habitat construction and in-situ resource utilization (ISRU); etc. These robots will be telesupervised by a small number of human ground controllers and/or astronauts, who will be able to share control with and teleoperate each individual robot whenever necessary, all from a safe, "shirtsleeve"
    To Boldly Go Where No Human-Robot Team Has Gone Before, Papers from the 2006 AAAI Spring Symposium, Technical Report SS-06-07, Stanford, California, USA, March 27-29, 2006; 01/2006
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    ABSTRACT: In January 2004, NASA began a bold enterprise to return to the Moon, and with the technologies and expertise gained, press on to Mars. The underlying Vision for Space Exploration calls for a sustained and affordable human and robotic program to explore the solar system and beyond; to conduct human expeditions to Mars after successfully demonstrating sustained human exploration missions on the Moon. The approach is to "send human and robotic explorers as partners, leveraging the capabilities of each where most useful. " Human-robot interfacing technologies for this approach are required at readiness levels above any available today. In this paper, we describe the HRI aspects of a robot supervision architecture we are developing under NASA's auspices, based on the authors'extensive experience with field deployment of ground, underwater, lighter-than-air, and inspection autonomous and semi-autonomous robotic vehicles and systems. This work was supported by NASA under CA #NNA05CP96A.
    Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, HRI 2006, Salt Lake City, Utah, USA, March 2-3, 2006; 01/2006
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    ABSTRACT: We are developing a Sensor Web-relevant system called the Telesupervised Adaptive Ocean Sensor Fleet that uses a group of National Oceanic and Atmospheric Administration extended-deployment autonomous surface vehicles to enable in-situ study of surface and sub-surface characteristics of Harmful Algal Blooms (HAB). The architecture supports adaptive reconfiguration based on environmental sensor inputs ("smart" sensing), and increases data-gathering effectiveness and science return while reducing demands on scientists for tasking, control, and monitoring. It combines and adapts prior related work done at Carnegie Mellon University, NASA Goddard Space Flight Center, Wallops Flight Facility, Emergent Space Technologies, and the Jet Propulsion Laboratory. Initial multi-vessel HAB characterization tests will be performed during summer 2007 with rhodamine dye as a HAB simulant and an airborne sensor validation system. The described architecture is broadly applicable to ecological forecasting, water management, carbon management, disaster management, coastal management, homeland security, and planetary exploration. Index Terms harmful algal blooms, sensor web, ocean sensing, adaptive sampling, telesupervision, multirobot systems.
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    ABSTRACT: This paper describes field test results to date using a multi-robot science exploration software architecture and system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the Ocean-Atmosphere Sensor Integration System (OASIS) platforms, to enable in situ study of phenomena in the ocean-atmosphere interface. TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a multi-level autonomy control architecture, where the operating mode of the vehicles ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We describe recent field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.