Nonlinear estimation methods for autonomous tracked vehicle with slip

Zhou, Bo; JD, Han

doi:10.3901/cjme.2007.04.001

Cited by 13 publications

(10 citation statements)

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“…This result also implies that when the parameter's sudden change occurs, such as k=100, the MIT-AESMF method's noise adaptive mechanism makes Qk closely approach the real boundary. Figure 3 clearly demonstrates, again, that because of the selective mechanism (21) in AESMF, when the parameter's sudden change happens, the initial process noise ellipsoidal boundary cannot include the span of the parameter variation and thereby causes the measurement to fail. Thus, just calculating the time update leads to the uncertain boundary's inflation, which remains for more than 20 sampling cycles until it includes the sudden change range, and the state estimation of AESMF becomes biased since there is no measurement updating.…”

Section: Conditionmentioning

confidence: 99%

“…Figures 5-a and 5-b demonstrate that, in the 200 simulation moments, and because the initial ellipsoidal boundary is set too small, AESMF is using this small ellipsoid to calculate, which leads to no overlaps between the state estimation and the measurement estimation ellipsoids. Therefore, it does not satisfy AESMF's selective updating requirements (21), and at the same time the measurement updating makes the uncertain boundary shrink to the initial small ellipsoid boundary. Thus, there are no overlaps in measurement updating.…”

Section: Conditionmentioning

confidence: 99%

“…Thus, the AESMF method employs this small ellipsoid in calculation, which causes the real value to be excluded from the estimation of the boundaries of the states and parameters, to fail the measure update requirements (21) and to stop the measurement updating. Ultimately, it causes uncertain boundaries to slowly inflate, and lasts for more than 10 sampling cycles until the boundaries include this sudden change range.…”

Section: Conditionmentioning

confidence: 99%

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A MIT-Based Nonlinear Adaptive Set-Membership Filter for the Ellipsoidal Estimation of Mobile Robots' States

Song

et al. 2012

International Journal of Advanced Robotic Systems

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The adaptive extended set-membership filter (AESMF) for nonlinear ellipsoidal estimation suffers a mismatch between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method-based adaptive set-membership filter, for the optimization of the set boundaries of process noise, is developed and applied to the nonlinear joint estimation of both time-varying states and parameters. As a result of using the proposed MIT-AESMF, the estimation effectiveness and boundary accuracy of traditional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method.

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Section: Conditionmentioning

confidence: 99%

Section: Conditionmentioning

confidence: 99%

Section: Conditionmentioning

confidence: 99%

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A MIT-Based Nonlinear Adaptive Set-Membership Filter for the Ellipsoidal Estimation of Mobile Robots' States

Song

et al. 2012

International Journal of Advanced Robotic Systems

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“…In this paper, this relatively simple model of the slipping will be used to stabilize the tracked robot, and our purpose is to give some preliminary results for the control of tracked robot with slipping. It should be noted that the slipping parameter can be estimated on-line by some nonlinear estimators such as extended Kalman filter [22] . The stabilization control problem of the tracked robot with slipping studied in this paper is stated as follows:…”

Section: Preliminary and Problem Statementmentioning

confidence: 99%

Backstepping based global exponential stabilization of a tracked mobile robot with slipping perturbation

Zhou¹,

Han²,

Duan³

2011

J Bionic Eng

Self Cite

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While the nonholonomic robots with no-slipping constraints are studied extensively nowadays, the slipping effect is inevitable in many practical applications and should be considered necessarily to achieve autonomous navigation and control purposes especially in outdoor environments. In this paper the robust point stabilization problem of a tracked mobile robot is discussed in the presence of track slipping, which can be treated as model perturbation that violates the pure nonholonomic constraints. The kinematic model of the tracked vehicle is created, in which the slipping is assumed to be a time-varying parameter under certain assumptions of track-soil interaction. By transforming the original system to the special chained form of nonholonomic system, the integrator backstepping procedure with a state-scaling technique is used to construct the controller to stabilize the system at the kinematic level. The global exponential stability of the final system can be guaranteed by Lyapunov theory. Simulation results with different initial states and slipping parameters demonstrate the fast convergence, robustness and insensitivity to the initial state of the proposed method.

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“…In the ESMF algorithm, the noise is assumed to be unknown but bounded (UBB), and an uncertain set that is guaranteed to contain the real system state is obtained at every time step [13]. Some comparative research on the ESMF and the Kalman-class schemes is available [14]. One of the main advantages of using ESMF for cooperative localization is that the fusion of information is easily realized through geometrical computations.…”

mentioning

confidence: 99%

Active Persistent Localization of a Three-Dimensional Moving Target Under Set-Membership Uncertainty Description Through Cooperation of Multiple Mobile Robots

Gu,

He,

Han

2015

IEEE Trans. Ind. Electron.

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Abstract-The persistent localization of moving targets is one of the most important applications of mobile robot systems. However, the sensors on each individual robot are often not sufficiently accurate for this task, presenting a severe limitation on the application of robots to many situations. To overcome this problem, systems of multiple robots have been established. These systems improve the localization accuracy of mobile targets by fusing together multiple sensing data. Numerous studies have demonstrated that the relative motion (or specifically, the relative pose) between the robot and the target, as well as that among different robots, strongly influences the final localization accuracy of the data fusion algorithm. In other words, the regulation of motion in a multiple-robot system will lead to better localization results. Thus, in this paper, a new active persistent localization (APL) scheme is proposed. The underlying concept of this scheme is based on set-membership descriptions of uncertainties. The basic problem is formulated in the framework of an enhanced set-membership filter, and a new data fusion algorithm with improved accuracy and convergence properties is designed. A motion-planning algorithm is then incorporated under the so-called optimal localization condition to complete the APL scheme. Simulations using multiple 3-D mobile robot systems and experiments on a multiple rotor-flying-robots test bed show that the proposed algorithm successfully enhances the accuracy of the persistent localization of the moving target.

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Nonlinear estimation methods for autonomous tracked vehicle with slip

Cited by 13 publications

References 0 publications

A MIT-Based Nonlinear Adaptive Set-Membership Filter for the Ellipsoidal Estimation of Mobile Robots' States

A MIT-Based Nonlinear Adaptive Set-Membership Filter for the Ellipsoidal Estimation of Mobile Robots' States

Backstepping based global exponential stabilization of a tracked mobile robot with slipping perturbation

Active Persistent Localization of a Three-Dimensional Moving Target Under Set-Membership Uncertainty Description Through Cooperation of Multiple Mobile Robots

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