Shared Understanding for Collaborative Control

Bruemmer, David J.; Few, Douglas A.; Boring, Ronald L.; Marble, Julie L.; Walton, Miles; Nielsen, Curtis W.

doi:10.1109/tsmca.2005.850599

Cited by 124 publications

(79 citation statements)

References 21 publications

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“…go there, or look here, grab that, etc). Our research efforts have been validated over numerous user studies including novices [3,17] and domain experts [4,5,18].…”

Section: Previous Work At the Inlmentioning

confidence: 99%

Using mixed-initiative human-robot interaction to bound performance in a search task

Nielsen

Few

Athey

2008

2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing

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“…go there, or look here, grab that, etc). Our research efforts have been validated over numerous user studies including novices [3,17] and domain experts [4,5,18].…”

Section: Previous Work At the Inlmentioning

confidence: 99%

Using mixed-initiative human-robot interaction to bound performance in a search task

Nielsen

Few

Athey

2008

2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing

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“…Importantly, humans are capable of intelligent and adaptable decision making in response to reasoning about realworld information in real-time. Progress has been reported on humans and robots in exploration [146,147,148,149,150] and how humans work together in search tasks [151,152,153,154]. However, more work is needed to leverage what cognitive scientists understand about human decision making and behavior.…”

Section: Recent Developments and Future Directionsmentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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“…[1][2][3][4]. In many applications, the mobile robot should be partially or fully operated by the human operator because of the technical limitations of the autonomous mobility.…”

Section: Introductionmentioning

confidence: 99%

Expanded Guide Circle-based Obstacle Avoidance for the Remotely Operated Mobile Robot

Park¹,

Kim²

2014

Journal of Electrical Engineering and Technology

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-For the remote operation of the mobile robot, the human operator depends fully on the sensory information which is the partial information of the workspace of the mobile robot. It is usually very hard to fully manually operate the mobile robot in this situation. We propose the efficient guidance navigation method for improving the efficiency of the remote operation with the expanded guide circle using the sensory information. The guidance command is generated from the proposed algorithm using the expanded guide circle. We evaluated the performance of the proposed algorithm using the experiments.

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Shared Understanding for Collaborative Control

Cited by 124 publications

References 21 publications

Using mixed-initiative human-robot interaction to bound performance in a search task

Using mixed-initiative human-robot interaction to bound performance in a search task

Cooperative Vehicle Environmental Monitoring

Expanded Guide Circle-based Obstacle Avoidance for the Remotely Operated Mobile Robot

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