An IMM algorithm with federated information mode‐matched filters for AGV

Abstract

In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) is presented. The developed navigation algorithm is an interacting multiple-model (IMM) algorithm used to detect other AGVs using fused information from multiple sensors. In order to detect other AGVs, two kinematic models were derived: A constant-velocity model for linear motion, and a constant-speed turn model for curvilinear motion. In the constant-speed turn model, a nonlinear information filter (IF) is used in place of the extended Kalman filter (KF). Being equivalent to the KF algebraically, the IF is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear IF. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information-sharing principle of the federated IF are discussed. The performance of the suggested algorithm using a Monte Carlo simulation is evaluated under the three navigation patterns.

RETRACTED: An enhanced obstacle avoidance and path correction mechanism for an autonomous intelligent robot with multiple sensors

“…Other notable results include an interactive multiple model algorithm (Kim and Hong, 2005), a three-layer control architecture (deliberative, sequencing, reflexive) (Hong et al, 2008), a collision-free motion coordination for multiple heterogeneous robots (Ko et al, 2008), a predictive navigation approach with non-holonomic and minimum turning radius constraints (Widyotriatmo et al, 2009), a time-varying feedback control via the chained form (Tamba et al, 2009), and others (Borenstein and Koren, 1998; Castillo et al, 2006; Chen et al, 2011a, 2011b, 2011c; Ngo and Hong, 2009; Ngo et al, 2009).…”

Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy

“…After decades of development, the Kalman filtering is very mature. Due to its remarkable estimation performance, the Kalman filtering is widely used in many areas [ 32 - 34 ] including neuroscience [ 24 , 25 , 35 , 36 ]. In the present study, the Kalman filter was used as a model coefficients estimator.…”

Adaptive Federated IMM Filter for AUV Integrated Navigation Systems

“…When the system has discrete uncertainties together with continuous uncertainties in the dynamic or measurement model, the IMM algorithm is a very effective method [32][33][34][35][36]. The IMM algorithm has shown superior performance with a low computational burden in a variety of applications, such as target tracking [37][38][39][40], mobile node localization [41], and motion planning [42]. However, there are few studies about the application of the IMM algorithm to the underwater navigation system.…”