The Experts below are selected from a list of 17307 Experts worldwide ranked by ideXlab platform

Christopher M Clark - One of the best experts on this subject based on the ideXlab platform.

  • tracking and following a tagged leopard shark with an autonomous underwater vehicle
    Journal of Field Robotics, 2013
    Co-Authors: Christopher M Clark, Dylan Shinzaki, Chris Gage, E Manii, Michael Farris, Christopher G Lowe, Chirstopher Forney, Mark Moline
    Abstract:

    This paper presents a prototype system that enables an autonomous underwater vehicle (AUV) to autonomously track and follow a shark that has been tagged with an acoustic transmitter. The AUV's onboard processor handles both real-time estimation of the shark's two-dimensional planar position, velocity, and orientation states, as well as a straightforward control scheme to drive the AUV toward the shark. The AUV is equipped with a stereo- hydrophone and receiver system that detects acoustic signals transmitted by the acoustic tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but it does not provide the sign (+ or −) of the Bearing Angle. Estimation is accomplished using a particle filter that fuses Bearing measurements over time to produce a state estimate of the tag location. The particle filter combined with a heuristic-based controller allows the system to overcome the ambiguity in the sign of the Bearing Angle. The state estimator and control scheme were validated by tracking both a stationary tag and a moving tag with known positions. Offline analysis of these data showed that state estimation can be improved by optimizing diffusion parameters in the prediction step of the filter, and considering signal strength of the acoustic signals in the resampling stage of the filter. These experiments revealed that state estimate errors were on the order of those obtained by current long-distance shark-tracking methods, i.e., manually driven boat-based tracking systems. Final experiments took place in SeaPlane Lagoon, Los Angeles, where a 1-m leopard shark (Triakis semifasciata) was caught, tagged, and released before being autonomously tracked and followed by the proposed AUV system for several hours. C

  • tracking of a tagged leopard shark with an auv sensor calibration and state estimation
    International Conference on Robotics and Automation, 2012
    Co-Authors: Chirstopher Forney, E Manii, Michael Farris, Christopher G Lowe, Christopher M Clark
    Abstract:

    Presented is a method for estimating the 2D planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but does not provide the sign (+ or -) of the Bearing Angle. A Particle Filter was used for fusing these measurements over time to produce a state estimate of the tag location. The Particle Filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the Bearing Angle. This state estimator was validated by tracking both a stationary tag and moving tag with known positions. These experiments revealed state estimate errors were on par with those obtained by manually driven boat based tracking systems, the current method used for tracking fish and sharks over long distances. Final experiments involved the catching, releasing, and an autonomous AUV tracking of a 1 meter Leopard Shark (Triakis semifasciata) in SeaPlane Lagoon, Los Angeles, California.

  • ICRA - Tracking of a tagged leopard shark with an AUV: Sensor calibration and state estimation
    2012 IEEE International Conference on Robotics and Automation, 2012
    Co-Authors: Chirstopher Forney, E Manii, Michael Farris, Christopher G Lowe, Christopher M Clark
    Abstract:

    Presented is a method for estimating the 2D planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but does not provide the sign (+ or -) of the Bearing Angle. A Particle Filter was used for fusing these measurements over time to produce a state estimate of the tag location. The Particle Filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the Bearing Angle. This state estimator was validated by tracking both a stationary tag and moving tag with known positions. These experiments revealed state estimate errors were on par with those obtained by manually driven boat based tracking systems, the current method used for tracking fish and sharks over long distances. Final experiments involved the catching, releasing, and an autonomous AUV tracking of a 1 meter Leopard Shark (Triakis semifasciata) in SeaPlane Lagoon, Los Angeles, California.

Chirstopher Forney - One of the best experts on this subject based on the ideXlab platform.

  • tracking and following a tagged leopard shark with an autonomous underwater vehicle
    Journal of Field Robotics, 2013
    Co-Authors: Christopher M Clark, Dylan Shinzaki, Chris Gage, E Manii, Michael Farris, Christopher G Lowe, Chirstopher Forney, Mark Moline
    Abstract:

    This paper presents a prototype system that enables an autonomous underwater vehicle (AUV) to autonomously track and follow a shark that has been tagged with an acoustic transmitter. The AUV's onboard processor handles both real-time estimation of the shark's two-dimensional planar position, velocity, and orientation states, as well as a straightforward control scheme to drive the AUV toward the shark. The AUV is equipped with a stereo- hydrophone and receiver system that detects acoustic signals transmitted by the acoustic tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but it does not provide the sign (+ or −) of the Bearing Angle. Estimation is accomplished using a particle filter that fuses Bearing measurements over time to produce a state estimate of the tag location. The particle filter combined with a heuristic-based controller allows the system to overcome the ambiguity in the sign of the Bearing Angle. The state estimator and control scheme were validated by tracking both a stationary tag and a moving tag with known positions. Offline analysis of these data showed that state estimation can be improved by optimizing diffusion parameters in the prediction step of the filter, and considering signal strength of the acoustic signals in the resampling stage of the filter. These experiments revealed that state estimate errors were on the order of those obtained by current long-distance shark-tracking methods, i.e., manually driven boat-based tracking systems. Final experiments took place in SeaPlane Lagoon, Los Angeles, where a 1-m leopard shark (Triakis semifasciata) was caught, tagged, and released before being autonomously tracked and followed by the proposed AUV system for several hours. C

  • tracking of a tagged leopard shark with an auv sensor calibration and state estimation
    International Conference on Robotics and Automation, 2012
    Co-Authors: Chirstopher Forney, E Manii, Michael Farris, Christopher G Lowe, Christopher M Clark
    Abstract:

    Presented is a method for estimating the 2D planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but does not provide the sign (+ or -) of the Bearing Angle. A Particle Filter was used for fusing these measurements over time to produce a state estimate of the tag location. The Particle Filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the Bearing Angle. This state estimator was validated by tracking both a stationary tag and moving tag with known positions. These experiments revealed state estimate errors were on par with those obtained by manually driven boat based tracking systems, the current method used for tracking fish and sharks over long distances. Final experiments involved the catching, releasing, and an autonomous AUV tracking of a 1 meter Leopard Shark (Triakis semifasciata) in SeaPlane Lagoon, Los Angeles, California.

  • ICRA - Tracking of a tagged leopard shark with an AUV: Sensor calibration and state estimation
    2012 IEEE International Conference on Robotics and Automation, 2012
    Co-Authors: Chirstopher Forney, E Manii, Michael Farris, Christopher G Lowe, Christopher M Clark
    Abstract:

    Presented is a method for estimating the 2D planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but does not provide the sign (+ or -) of the Bearing Angle. A Particle Filter was used for fusing these measurements over time to produce a state estimate of the tag location. The Particle Filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the Bearing Angle. This state estimator was validated by tracking both a stationary tag and moving tag with known positions. These experiments revealed state estimate errors were on par with those obtained by manually driven boat based tracking systems, the current method used for tracking fish and sharks over long distances. Final experiments involved the catching, releasing, and an autonomous AUV tracking of a 1 meter Leopard Shark (Triakis semifasciata) in SeaPlane Lagoon, Los Angeles, California.

Maarouf Saad - One of the best experts on this subject based on the ideXlab platform.

  • adaptive leader follower formation control of underactuated surface vessels under asymmetric range and Bearing constraints
    IEEE Transactions on Vehicular Technology, 2018
    Co-Authors: Jawhar Ghommam, Maarouf Saad
    Abstract:

    This paper deals with the problem of leader–follower formation control for a group of underactuated surface vessels with partially known control input functions. In the proposed scheme, the problem is formulated as an adaptive feedback control problem for aLine-Of-Sight (LOS) based formation configuration of a leader and a follower. To account for LOS and Bearing Angle time-varying constraints, asymmetric barrier Lyapunov functions are incorporated with the control design. Furthermore, in order to alleviate required velocity information on the leader, a reconstruction module is designed to estimate the vector velocity of this leader. This reconstruction is accomplished in finite time with zero error, which allows the injection of accurate estimation into the formation controller. The controller is then developed within the framework of the backstepping technique, with the parametric uncertainties and the unknown gains being estimated by a novel structure identifier. The overall closed-loop system, is proved to be semiglobally uniformly ultimately bounded by Lyapunov stability theory. Furthermore, we show under the proposed control scheme that the constraints requirement on the LOS range and Bearing Angle tracking errors are not violated during the formation process. Finally, the effectiveness and the robustness of the proposed strategy are exhibited through simulations.

  • Adaptive Leader–Follower Formation Control of Underactuated Surface Vessels Under Asymmetric Range and Bearing Constraints
    IEEE Transactions on Vehicular Technology, 2018
    Co-Authors: Jawhar Ghommam, Maarouf Saad
    Abstract:

    This paper deals with the problem of leader–follower formation control for a group of underactuated surface vessels with partially known control input functions. In the proposed scheme, the problem is formulated as an adaptive feedback control problem for aLine-Of-Sight (LOS) based formation configuration of a leader and a follower. To account for LOS and Bearing Angle time-varying constraints, asymmetric barrier Lyapunov functions are incorporated with the control design. Furthermore, in order to alleviate required velocity information on the leader, a reconstruction module is designed to estimate the vector velocity of this leader. This reconstruction is accomplished in finite time with zero error, which allows the injection of accurate estimation into the formation controller. The controller is then developed within the framework of the backstepping technique, with the parametric uncertainties and the unknown gains being estimated by a novel structure identifier. The overall closed-loop system, is proved to be semiglobally uniformly ultimately bounded by Lyapunov stability theory. Furthermore, we show under the proposed control scheme that the constraints requirement on the LOS range and Bearing Angle tracking errors are not violated during the formation process. Finally, the effectiveness and the robustness of the proposed strategy are exhibited through simulations.

E Manii - One of the best experts on this subject based on the ideXlab platform.

  • tracking and following a tagged leopard shark with an autonomous underwater vehicle
    Journal of Field Robotics, 2013
    Co-Authors: Christopher M Clark, Dylan Shinzaki, Chris Gage, E Manii, Michael Farris, Christopher G Lowe, Chirstopher Forney, Mark Moline
    Abstract:

    This paper presents a prototype system that enables an autonomous underwater vehicle (AUV) to autonomously track and follow a shark that has been tagged with an acoustic transmitter. The AUV's onboard processor handles both real-time estimation of the shark's two-dimensional planar position, velocity, and orientation states, as well as a straightforward control scheme to drive the AUV toward the shark. The AUV is equipped with a stereo- hydrophone and receiver system that detects acoustic signals transmitted by the acoustic tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but it does not provide the sign (+ or −) of the Bearing Angle. Estimation is accomplished using a particle filter that fuses Bearing measurements over time to produce a state estimate of the tag location. The particle filter combined with a heuristic-based controller allows the system to overcome the ambiguity in the sign of the Bearing Angle. The state estimator and control scheme were validated by tracking both a stationary tag and a moving tag with known positions. Offline analysis of these data showed that state estimation can be improved by optimizing diffusion parameters in the prediction step of the filter, and considering signal strength of the acoustic signals in the resampling stage of the filter. These experiments revealed that state estimate errors were on the order of those obtained by current long-distance shark-tracking methods, i.e., manually driven boat-based tracking systems. Final experiments took place in SeaPlane Lagoon, Los Angeles, where a 1-m leopard shark (Triakis semifasciata) was caught, tagged, and released before being autonomously tracked and followed by the proposed AUV system for several hours. C

  • tracking of a tagged leopard shark with an auv sensor calibration and state estimation
    International Conference on Robotics and Automation, 2012
    Co-Authors: Chirstopher Forney, E Manii, Michael Farris, Christopher G Lowe, Christopher M Clark
    Abstract:

    Presented is a method for estimating the 2D planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but does not provide the sign (+ or -) of the Bearing Angle. A Particle Filter was used for fusing these measurements over time to produce a state estimate of the tag location. The Particle Filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the Bearing Angle. This state estimator was validated by tracking both a stationary tag and moving tag with known positions. These experiments revealed state estimate errors were on par with those obtained by manually driven boat based tracking systems, the current method used for tracking fish and sharks over long distances. Final experiments involved the catching, releasing, and an autonomous AUV tracking of a 1 meter Leopard Shark (Triakis semifasciata) in SeaPlane Lagoon, Los Angeles, California.

  • ICRA - Tracking of a tagged leopard shark with an AUV: Sensor calibration and state estimation
    2012 IEEE International Conference on Robotics and Automation, 2012
    Co-Authors: Chirstopher Forney, E Manii, Michael Farris, Christopher G Lowe, Christopher M Clark
    Abstract:

    Presented is a method for estimating the 2D planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but does not provide the sign (+ or -) of the Bearing Angle. A Particle Filter was used for fusing these measurements over time to produce a state estimate of the tag location. The Particle Filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the Bearing Angle. This state estimator was validated by tracking both a stationary tag and moving tag with known positions. These experiments revealed state estimate errors were on par with those obtained by manually driven boat based tracking systems, the current method used for tracking fish and sharks over long distances. Final experiments involved the catching, releasing, and an autonomous AUV tracking of a 1 meter Leopard Shark (Triakis semifasciata) in SeaPlane Lagoon, Los Angeles, California.

Christopher G Lowe - One of the best experts on this subject based on the ideXlab platform.

  • tracking and following a tagged leopard shark with an autonomous underwater vehicle
    Journal of Field Robotics, 2013
    Co-Authors: Christopher M Clark, Dylan Shinzaki, Chris Gage, E Manii, Michael Farris, Christopher G Lowe, Chirstopher Forney, Mark Moline
    Abstract:

    This paper presents a prototype system that enables an autonomous underwater vehicle (AUV) to autonomously track and follow a shark that has been tagged with an acoustic transmitter. The AUV's onboard processor handles both real-time estimation of the shark's two-dimensional planar position, velocity, and orientation states, as well as a straightforward control scheme to drive the AUV toward the shark. The AUV is equipped with a stereo- hydrophone and receiver system that detects acoustic signals transmitted by the acoustic tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but it does not provide the sign (+ or −) of the Bearing Angle. Estimation is accomplished using a particle filter that fuses Bearing measurements over time to produce a state estimate of the tag location. The particle filter combined with a heuristic-based controller allows the system to overcome the ambiguity in the sign of the Bearing Angle. The state estimator and control scheme were validated by tracking both a stationary tag and a moving tag with known positions. Offline analysis of these data showed that state estimation can be improved by optimizing diffusion parameters in the prediction step of the filter, and considering signal strength of the acoustic signals in the resampling stage of the filter. These experiments revealed that state estimate errors were on the order of those obtained by current long-distance shark-tracking methods, i.e., manually driven boat-based tracking systems. Final experiments took place in SeaPlane Lagoon, Los Angeles, where a 1-m leopard shark (Triakis semifasciata) was caught, tagged, and released before being autonomously tracked and followed by the proposed AUV system for several hours. C

  • tracking of a tagged leopard shark with an auv sensor calibration and state estimation
    International Conference on Robotics and Automation, 2012
    Co-Authors: Chirstopher Forney, E Manii, Michael Farris, Christopher G Lowe, Christopher M Clark
    Abstract:

    Presented is a method for estimating the 2D planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but does not provide the sign (+ or -) of the Bearing Angle. A Particle Filter was used for fusing these measurements over time to produce a state estimate of the tag location. The Particle Filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the Bearing Angle. This state estimator was validated by tracking both a stationary tag and moving tag with known positions. These experiments revealed state estimate errors were on par with those obtained by manually driven boat based tracking systems, the current method used for tracking fish and sharks over long distances. Final experiments involved the catching, releasing, and an autonomous AUV tracking of a 1 meter Leopard Shark (Triakis semifasciata) in SeaPlane Lagoon, Los Angeles, California.

  • ICRA - Tracking of a tagged leopard shark with an AUV: Sensor calibration and state estimation
    2012 IEEE International Conference on Robotics and Automation, 2012
    Co-Authors: Chirstopher Forney, E Manii, Michael Farris, Christopher G Lowe, Christopher M Clark
    Abstract:

    Presented is a method for estimating the 2D planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative Bearing Angle to the tag, but does not provide the sign (+ or -) of the Bearing Angle. A Particle Filter was used for fusing these measurements over time to produce a state estimate of the tag location. The Particle Filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the Bearing Angle. This state estimator was validated by tracking both a stationary tag and moving tag with known positions. These experiments revealed state estimate errors were on par with those obtained by manually driven boat based tracking systems, the current method used for tracking fish and sharks over long distances. Final experiments involved the catching, releasing, and an autonomous AUV tracking of a 1 meter Leopard Shark (Triakis semifasciata) in SeaPlane Lagoon, Los Angeles, California.