Visual Feedback for Nonholonomic Mobile Robots

2010 ◽  
pp. 152-181 ◽  
Author(s):  
Andrea Usai ◽  
Paolo Di Giamberardino
Keyword(s):  
Mobile Robots ◽  
State Estimation ◽  
Visual Feedback ◽  
Fundamental Matrix ◽  
Experimental Results ◽  
System State ◽  
Head Pose ◽  
Nonholonomic Mobile Robots ◽  
Camera System ◽  
Observer Accuracy

In this chapter, we describe a homography approach to vision based feedback for nonholonomic mobile robots control. Differently than other approaches based on homography or fundamental matrix, our method has been developed to be robust to reference features loss, during the robot movement. This allows us to implement an arbitrary control law without the need of a teach-by-showing stage. In the chapter, the use of a stereo camera system to improve the observer accuracy and to perform an auto-calibration of the stereo-head pose is investigated. Experimental results are provided to show the performances of the proposed system state estimation, using an eye-in-hand mobile robotic platform.

Simulation and Experimental Results of a New Control Strategy For Point Stabilization of Nonholonomic Mobile Robots

2020 ◽  
Vol 67 (8) ◽  
pp. 6679-6687 ◽  
Author(s):  
Ernesto Fabregas ◽  
Gonzalo Farias ◽  
Ernesto Aranda-Escolastico ◽  
Gonzalo Garcia ◽  
Dictino Chaos ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Control Strategy ◽  
Experimental Results ◽  
Nonholonomic Mobile Robots ◽  
Point Stabilization

scholarly journals Practical Stabilization of Uncertain Nonholonomic Mobile Robots Based on Visual Servoing Model with Uncalibrated Camera Parameters

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hua Chen ◽  
Bingyan Chen ◽  
Baojun Li ◽  
Jinbo Zhang
Keyword(s):  
Mobile Robots ◽  
Visual Servoing ◽  
Kinematic Model ◽  
Small Neighborhood ◽  
Angle Measurement ◽  
Control Technique ◽  
Stabilization Problem ◽  
Nonholonomic Mobile Robots ◽  
Camera System ◽  
Practical Stabilization

The practical stabilization problem is addressed for a class of uncertain nonholonomic mobile robots with uncalibrated visual parameters. Based on the visual servoing kinematic model, a new switching controller is presented in the presence of parametric uncertainties associated with the camera system. In comparison with existing methods, the new design method is directly used to control the original system without any state or input transformation, which is effective to avoid singularity. Under the proposed control law, it is rigorously proved that all the states of closed-loop system can be stabilized to a prescribed arbitrarily small neighborhood of the zero equilibrium point. Furthermore, this switching control technique can be applied to solve the practical stabilization problem of a kind of mobile robots with uncertain parameters (and angle measurement disturbance) which appeared in some literatures such as Morin et al. (1998), Hespanha et al. (1999), Jiang (2000), and Hong et al. (2005). Finally, the simulation results show the effectiveness of the proposed controller design approach.

scholarly journals Randomly Weighted CKF for Multisensor Integrated Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Hua Zong ◽  
Zhaohui Gao ◽  
Wenhui Wei ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Estimation Method ◽  
Experimental Results ◽  
Integrated Systems ◽  
System State ◽  
System Error ◽  
Random Weights ◽  
Cubature Kalman Filter ◽  
Dynamic Modification

The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

POWER SYSTEM STATE ESTIMATION USING A ROBUST ESTIMATOR

2019 ◽  
Vol XVI (4) ◽  
pp. 53-65
Author(s):  
Zahid Khan ◽  
Katrina Lane Krebs ◽  
Sarfaraz Ahmad ◽  
Misbah Munawar
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Loss Function ◽  
Weighted Least Squares ◽  
Primary Data ◽  
Robust Estimator ◽  
System State ◽  
State Estimator ◽  
Suggested Approach ◽  
Power System State Estimation

State estimation (SE) is a primary data processing algorithm which is utilised by the control centres of advanced power systems. The most generally utilised state estimator is based on the weighted least squares (WLS) approach which is ineffective in addressing gross errors of input data of state estimator. This paper presents an innovative robust estimator for SE environments to overcome the non-robustness of the WLS estimator. The suggested approach not only includes the similar functioning of the customary loss function of WLS but also reflects loss function built on the modified WLS (MWLS) estimator. The performance of the proposed estimator was assessed based on its ability to decrease the impacts of gross errors on the estimation results. The properties of the suggested state estimator were investigated and robustness of the estimator was studied considering the influence function. The effectiveness of the proposed estimator was demonstrated with the help of examples which also indicated non-robustness of MWLS estimator in SE algorithm.

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