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Cooperative distributed estimation and control of multiple autonomous vehicles for range-based underwater target localization and pursuit
EUMarineRobots webinar
Speaker:Nguyen Tuan Hung
Contact: [email protected]
Joint work with: Francisco Rego, Naveen Crasta, David Salinas, Antonio Pascoal, Tor Johansen
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Cooperative target localization and pursuit Introduction▷ Introduction
Target
Problems
● How to localize the targets cooperatively ?
►ensure that all vehicles have an agreement on the estimates of the targets’ states
● How to pursue the targets cooperatively ?
► keep the vehicles close to the targets
►Keep the vehicles in a desired geometrical formation w.r.t. the targets
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Target localization and pursuit Introduction▷ Introduction
Target
Solutions
● Centralized approach with tools from
►Estimation theory (CRLB)
►Model predictive control
● Distributed approach with tools from
►Distributed estimation (distributed EKF)
►Distributed control
►Nonlinear control
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Centralized approach with MPC and CRLB
Target
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Target information measured by the Fisher Information Matrix (FIM)▷ Introduction
True target
Can not distinguish “True target” and Target’s image when using range measurements
(because the distances to the vehicle are equal)
Target’s image
Vehicle
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Target information measured by the Fisher Information Matrix (FIM)▷ Introduction
True target
Vehicle How to quantify the range-related information available to estimate the target´s state ?
Fisher Information Matrix
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Range-based SLAP using posterior CRLB Problem formulation▷ Introduction
Target
State
Input
Position
StatePosition noise
Prior information
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Range-based SLAP using posterior CRLB Problem formulation▷ Introduction
State
Input
Position
StatePosition noise
Prior information
Problem 1 [Range-based SLAP]
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Range-based SLAP using posterior CRLB ▷ Main idea
State
Input
Position
StatePosition noise
Prior information
Fisher Information Matrix (FIM)
Tychavsky et al., 1998
Objective
FIM depends on the vehicles’ positions driven by the vehicles’ inputs
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Range-based SLAP using posterior CRLB ▷ MPC scheme
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Range-based SLAP using posterior CRLB ▷ Example
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Range-based SLAP using posterior CRLB ▷ Example
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Summary
Reference: Nguyen T. Hung, N. Crasta, David Moreno-Salinas, António M. Pascoal, Tor A. Johansen, “Range-based target localization and pursuit with autonomous vehicles: An approach using posterior CRLB and model predictive control”, Robotics and Autonomous Systems, 2020.
● MPC + CRLB is a universal approach
● Results give useful guidelines for motion planning
● However, for the case of multiple trackers, the MPC scheme is implemented in a centralized manner
See an alternative (distributed) approach next
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Distributed Approach
Target
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DEC strategy for cooperative SLAP Problem formulation ▷ Introduction
Target noise
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DEC strategy for cooperative SLAP Problem formulation ▷ Introduction
Problem 2 [Cooperative distributed SLAP]
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DEC strategy for cooperative SLAP Problem formulation ▷ Introduction
“Optimal” vehicles-target relative geometry for target localization purposes [from the MPC approach]
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DEC strategy for cooperative SLAP Trajectory planning ▷ Introduction
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DEC strategy for cooperative SLAP Desired formation ▷ Introduction
Problem 2.1 [Cooperative control for target pursuit]
2 vehicles: 3 vehicles:
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DEC strategy for cooperative SLAP Proposed DEC architecture ▷ Introduction
Tracking Controller
Cooperative Estimation
(DEKF)
Cooperative Control
Network
Cooperative-layer
Tracking-layer
PDF: Probability density function
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DEC strategy for cooperative SLAP Cooperative estimation mechanism ▷ Introduction
Tracking Controller
Cooperative Estimation
(DEKF)
Cooperative Control
Network
Cooperative-layer
Tracking-layer
Cooperative Estimation
(DEKF)
Vehicle i
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DEC strategy for cooperative SLAP Cooperative control▷ Introduction
Tracking Controller
Cooperative Estimation
(DEKF)
Cooperative Control
Network
Cooperative-layer
Tracking-layer
Cooperative Estimation
(DEKF)
Vehicle i
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DEC strategy for cooperative SLAP Simulation with a single vehicle ▷ Introduction
Estimated target(star-red)
Target(blue)
Vehicle (solid red)
S-T curve (dash red)
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DEC strategy for cooperative SLAP Simulation with 3 vehicles ▷ Introduction
12
3
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DEC strategy for cooperative SLAP Simulation with a single vehicle ▷ Introduction
Vehicle (solid red)
Target (blue)
S-T curve(dash-red)
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DEC strategy for cooperative SLAP Simulation with three vehicle ▷ Introduction
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Summary
Future research directions and plans:
➢ Extend to the case of multiple targets
➢ Event driven sampling (measurement strategy)
➢ Conduct field trials at Expo, Lisbon
Materials about this work can be found at:
https://nt-hung.github.io/research/Range-based-target-localization/
Reference: Nguyen T. Hung, Francisco Rego, Antonio M. Pascoal, “Cooperative distributed estimation and control of multiple autonomous vehicles for range-based underwater target localization and pursuit”, IEEE Transactions on Control Systems and Technology, conditionally accepted with minor revision.
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Thank you!
Discussion