The Experts below are selected from a list of 25656 Experts worldwide ranked by ideXlab platform
Elizabeth A. Croft - One of the best experts on this subject based on the ideXlab platform.
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Sensor Uncertainty management for an encapsulated logical device architecture. Part II: a control policy for Sensor Uncertainty
Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001Co-Authors: D. Langlois, J.d. Elliott, Elizabeth A. CroftAbstract:For Part I see ACC, Arlington, VA. USA (2001). A procedure to perform data fusion inside a low-level control loop was developed and implemented on a 1-DOF manipulator. This procedure uses Sensory data provided by low-level and non-dedicated high-level Sensors, at different rates. Fusion of the multiple feedback signals generates a signal with a smaller Uncertainty level. The performance of the control scheme is directly related to the quality and relevance of the feedback signal. In this control policy, data fusion is performed with the data coming from the different Sensors, once they have been time-correlated using Kalman filters. Also, In order to stabilize the fused feedback signal when there is no data available from the slower Sensors, a Kalman filter is used to observe and generate a prediction of the fused measurement signal, which can then be used by the data fusion process. The slow Sensor processing delay compensation and fused measurement stabilization are independent of the fusion process. Therefore, any data fusion process can be used with this procedure, as long as the process respects the real-time constraint of the low-level control loop.
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Sensor Uncertainty management for an encapsulated logical device architecture: Part I - fusion of uncertain Sensor data
Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001Co-Authors: J.d. Elliott, D. Langlois, Elizabeth A. CroftAbstract:A systematic method of integrating high-level decision making and planning systems with low-level sensing, actuation and control is essential for the efficient implementation and maintenance of intelligent industrial automation systems. Additionally, for increased reliability in operation, a system should consider data as uncertain and all decisions should be made using data of an appropriate level of certainty. In this paper the encapsulated logical device (ELD) architecture is presented as an architecture that is modular and scalable. The ELD architecture allows the various agents in the architecture to be implemented in a distributed fashion on multiple hardware and software platforms. Additionally, the ELD contains a fusion mechanism that manages and propagates uncertain data throughout the architecture. Data and knowledge Uncertainty is represented in this architecture using Uncertainty ellipsoids. Finally, the ELD architecture bridges low-level real-time control with high-level event-driven decision-making and planning.
J.d. Elliott - One of the best experts on this subject based on the ideXlab platform.
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Sensor Uncertainty management for an encapsulated logical device architecture. Part II: a control policy for Sensor Uncertainty
Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001Co-Authors: D. Langlois, J.d. Elliott, Elizabeth A. CroftAbstract:For Part I see ACC, Arlington, VA. USA (2001). A procedure to perform data fusion inside a low-level control loop was developed and implemented on a 1-DOF manipulator. This procedure uses Sensory data provided by low-level and non-dedicated high-level Sensors, at different rates. Fusion of the multiple feedback signals generates a signal with a smaller Uncertainty level. The performance of the control scheme is directly related to the quality and relevance of the feedback signal. In this control policy, data fusion is performed with the data coming from the different Sensors, once they have been time-correlated using Kalman filters. Also, In order to stabilize the fused feedback signal when there is no data available from the slower Sensors, a Kalman filter is used to observe and generate a prediction of the fused measurement signal, which can then be used by the data fusion process. The slow Sensor processing delay compensation and fused measurement stabilization are independent of the fusion process. Therefore, any data fusion process can be used with this procedure, as long as the process respects the real-time constraint of the low-level control loop.
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Sensor Uncertainty management for an encapsulated logical device architecture: Part I - fusion of uncertain Sensor data
Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001Co-Authors: J.d. Elliott, D. Langlois, Elizabeth A. CroftAbstract:A systematic method of integrating high-level decision making and planning systems with low-level sensing, actuation and control is essential for the efficient implementation and maintenance of intelligent industrial automation systems. Additionally, for increased reliability in operation, a system should consider data as uncertain and all decisions should be made using data of an appropriate level of certainty. In this paper the encapsulated logical device (ELD) architecture is presented as an architecture that is modular and scalable. The ELD architecture allows the various agents in the architecture to be implemented in a distributed fashion on multiple hardware and software platforms. Additionally, the ELD contains a fusion mechanism that manages and propagates uncertain data throughout the architecture. Data and knowledge Uncertainty is represented in this architecture using Uncertainty ellipsoids. Finally, the ELD architecture bridges low-level real-time control with high-level event-driven decision-making and planning.
D. Langlois - One of the best experts on this subject based on the ideXlab platform.
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Sensor Uncertainty management for an encapsulated logical device architecture. Part II: a control policy for Sensor Uncertainty
Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001Co-Authors: D. Langlois, J.d. Elliott, Elizabeth A. CroftAbstract:For Part I see ACC, Arlington, VA. USA (2001). A procedure to perform data fusion inside a low-level control loop was developed and implemented on a 1-DOF manipulator. This procedure uses Sensory data provided by low-level and non-dedicated high-level Sensors, at different rates. Fusion of the multiple feedback signals generates a signal with a smaller Uncertainty level. The performance of the control scheme is directly related to the quality and relevance of the feedback signal. In this control policy, data fusion is performed with the data coming from the different Sensors, once they have been time-correlated using Kalman filters. Also, In order to stabilize the fused feedback signal when there is no data available from the slower Sensors, a Kalman filter is used to observe and generate a prediction of the fused measurement signal, which can then be used by the data fusion process. The slow Sensor processing delay compensation and fused measurement stabilization are independent of the fusion process. Therefore, any data fusion process can be used with this procedure, as long as the process respects the real-time constraint of the low-level control loop.
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Sensor Uncertainty management for an encapsulated logical device architecture: Part I - fusion of uncertain Sensor data
Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001Co-Authors: J.d. Elliott, D. Langlois, Elizabeth A. CroftAbstract:A systematic method of integrating high-level decision making and planning systems with low-level sensing, actuation and control is essential for the efficient implementation and maintenance of intelligent industrial automation systems. Additionally, for increased reliability in operation, a system should consider data as uncertain and all decisions should be made using data of an appropriate level of certainty. In this paper the encapsulated logical device (ELD) architecture is presented as an architecture that is modular and scalable. The ELD architecture allows the various agents in the architecture to be implemented in a distributed fashion on multiple hardware and software platforms. Additionally, the ELD contains a fusion mechanism that manages and propagates uncertain data throughout the architecture. Data and knowledge Uncertainty is represented in this architecture using Uncertainty ellipsoids. Finally, the ELD architecture bridges low-level real-time control with high-level event-driven decision-making and planning.
Hassan A Karimi - One of the best experts on this subject based on the ideXlab platform.
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a global path planner for safe navigation of autonomous vehicles in uncertain environments
Sensors, 2020Co-Authors: Mohammed Alharbi, Hassan A KarimiAbstract:Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under Sensor Uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes Sensor Uncertainty and determines optimal paths. The path planner has two components: Sensor analytics and path finder. The Sensor analytics component combines the uncertainties of all Sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder component then utilizes the acquired Sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches.
Mohammed Alharbi - One of the best experts on this subject based on the ideXlab platform.
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a global path planner for safe navigation of autonomous vehicles in uncertain environments
Sensors, 2020Co-Authors: Mohammed Alharbi, Hassan A KarimiAbstract:Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under Sensor Uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes Sensor Uncertainty and determines optimal paths. The path planner has two components: Sensor analytics and path finder. The Sensor analytics component combines the uncertainties of all Sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder component then utilizes the acquired Sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches.
