Robust Dead Reckoning System with Interacting Multiple Model Based Sensor Fault Diagnosis for Mobile Robot

2001 ◽  
Vol 34 (19) ◽  
pp. 155-160
Author(s):  
Masafumi Hashimoto ◽  
Hiroyuki Kawashima ◽  
Fuminori Oba
Keyword(s):  
Fault Diagnosis ◽  
Mobile Robot ◽  
Dead Reckoning ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Sensor Fault ◽  
Model Based

Sensor Fault Detection and Diagnosis in Dead Reckoning System of Mobile Robot: Interacting Multiple-Model Approach

2002 ◽  
Vol 14 (4) ◽  
pp. 342-348
Author(s):  
Masafumi Hashimoto ◽  
◽  
Hiroyuki Kawashima ◽  
Fuminori Oba ◽  
Keyword(s):  
Fault Detection ◽  
Mobile Robot ◽  
Failure Modes ◽  
Dead Reckoning ◽  
Fault Detection And Diagnosis ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Detection And Diagnosis

An interacting multiple-model (IMM) approach to sensor fault detection and diagnosis (FDD) in dead reckoning is proposed for navigating mobile robots. In this approach, changes of sensor normal/failure modes are explicitly modeled as switching from one mode to another in a probabilistic manner, and the sensor FDD and state estimate are achieved via a bank of parallel Kalman filters. To provide better FDD performance, mode probability averaging and heuristic decisionmaking logic are combined with the IMM based FDD algorithm. The proposed FDD is implemented on a skid-steered mobile robot, where 32 system modes (one normal mode and 31 hard sensor failure modes) of 5 sensors (4 wheel-encoders and one yaw-rate gyro) are handled. Experimental results validate the effectiveness of the proposed FDD.

scholarly journals Robust Dead-Reckoning System with Function on Sensor-Fault Diagnosis for Mobile Robot.

2002 ◽  
Vol 68 (666) ◽  
pp. 476-482
Author(s):  
Masafumi HASHIMOTO ◽  
Hiroyuki KAWASHIMA ◽  
Takashi NAKAGAMI ◽  
Fuminori OBA
Keyword(s):  
Fault Diagnosis ◽  
Mobile Robot ◽  
Dead Reckoning ◽  
Sensor Fault

An Interacting Multiple-Model-Based Algorithm for Driver Behavior Characterization Using Handling Risk

2019 ◽  
Vol 20 (12) ◽  
pp. 4308-4317 ◽  
Author(s):  
Sanghyun Hong ◽  
Jianbo Lu ◽  
Smruti R. Panigrahi ◽  
Jonathan Scott ◽  
Dimitar P. Filev
Keyword(s):  
Driver Behavior ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Model Based

IMM-UKF-TFS Model-based Approach for Intelligent Navigation

2013 ◽  
Vol 66 (6) ◽  
pp. 859-877 ◽  
Author(s):  
M. Malleswaran ◽  
V. Vaidehi ◽  
S. Irwin ◽  
B. Robin
Keyword(s):  
Kalman Filter ◽  
Constant Velocity ◽  
Unscented Kalman Filter ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
State Variables ◽  
Constant Acceleration ◽  
Positional Accuracy ◽  
Model Based ◽  
Novel Approach

This paper aims to introduce a novel approach named IMM-UKF-TFS (Interacting Multiple Model-Unscented Kalman Filter-Two Filter Smoother) to attain positional accuracy in the intelligent navigation of a manoeuvring vehicle. Here, the navigation filter is designed with an Unscented Kalman Filter (UKF), together with an Interacting Multiple Model algorithm (IMM), which estimates the state variables and handles the noise uncertainty of the manoeuvring vehicle. A model-based estimator named Two Filter Smoothing (TFS) is implemented along with the UKF-based IMM to improve positional accuracy. The performance of the proposed IMM-UKF-TFS method is verified by modelling the vehicle motion into Constant Velocity-Coordinated Turn (CV-CT), Constant Velocity – Constant Acceleration (CV-CA) and Constant Acceleration-Coordinated Turn (CA-CT) models. The simulation results proved that the proposed IMM-UKF-TFS gives better positional accuracy than the existing conventional estimators such as UKF and IMM-UKF.

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