Abstract
In this paper, a tracking algorithm for the autonomous navigation of the automated guided vehicles (AGVs) operated in a container terminal is investigated. The navigation system is based on sensors that detect range and bearing. The navigation algorithm used is an interacting multiple model algorithm to detect other AGVs and avoid obstacles using information obtained from the multiple sensors. In order to detect other AGVs (or obstacles), two kinematic models are derived: A constant velocity model for linear motion and a constant-speed turn model for curvilinear motion. For the constant-speed turn model, an unscented Kalman filter is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.
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Abbreviations
- a :
-
Augmented value
- f[k- 1,x(k-1)]:
-
Nonlinear vector-valued function at timek - 1
- \(f_{x_i^a (k)}^a \) :
-
Jacobian matrix
- G(k):
-
Noise transition matrix
- g[k-1,x(k-1)]:
-
Nonlinear vector-valued function at timek-1
- H :
-
Measurement matrix
- h[k, x(k)]:
-
Nonlinear measurement function
- K(k):
-
Kalman filter gain
- M :
-
Set of modal state
- m(k):
-
Scalar-valued modal state at timek
- P • :
-
Probability
- Px :
-
Covariance ofx
- p :
-
Nonlinear transformation
- Q(k):
-
Process noise covariance
- R(k):
-
Measurement noise covariance
- Wi :
-
Weights of sigma points
- w(k):
-
Measurement noise sequence
- x(k):
-
System base state vector at timek
- x :
-
Mean ofx
- z(k):
-
Noisy measurement vector at timek
- ξ(k),η(k):
-
Orthogonal coordinates of the horizontal plane
- k :
-
Scaling parameter
- μj :
-
Mode probability
- ν (k-1) :
-
process noise sequence
- πij :
-
Model transition probability
- Xi :
-
Sigma points
- ω(k):
-
Yaw rate at timek
- 2111; i :
-
Measurement sigma point
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Kim, YS., Hong, KS. A tracking algorithm for autonomous navigation of AGVs in an automated container terminal. J Mech Sci Technol 19, 72–86 (2005). https://doi.org/10.1007/BF02916106
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DOI: https://doi.org/10.1007/BF02916106