A backstepping non-linear controller for a mobile robot implemented in ROS

Barros, Taiser Tadeu Teixeira; Lages, Walter Fetter

doi:10.1109/indin.2014.6945529

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…However, the control law equations (16) and (17) can be extended to include the dynamics of the mobile robots. See the work done by Barros and Lages 19,20 for details. The input belief, obtained by computing equations (16) and (17) for each state particle, cannot be considered a result of the control scheme the way the state belief can for estimation, as it is obvious that the system takes a single two-dimensional vector as input.…”

Section: Control Using a Cloud Of Particlesmentioning

confidence: 99%

“…with h, g 1 , and g 2 > 0, makes equation ( 15) asymptotically stable. 19,20 As a consequence, the input belief contains control signals related to point stabilization of the state particles under no state transition uncertainty, that is to say, assuming a deterministic system with known parameters. Note that even though equation ( 15) is discontinuous at the origin, due to e in the denominator, the closed loop system is not.…”

Section: Control Using a Cloud Of Particlesmentioning

confidence: 99%

“…However, the control law equations ( 16) and ( 17) can be extended to include the dynamics of the mobile robots. See the work done by Barros and Lages 19,20 for details.…”

Section: Control Using a Cloud Of Particlesmentioning

confidence: 99%

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Differential-drive mobile robot control using a cloud of particles approach

Lages

Alves

2016

International Journal of Advanced Robotic Systems

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Common control systems for mobile robots include the use of some deterministic control law coupled with some pose estimation method, such as the extended Kalman filter, by considering the certainty equivalence principle. Recent approaches consider the use of partially observable Markov decision process strategies together with Bayesian estimators. These methods are well suited to handle the uncertainty in pose estimation but demand significant processing power. In order to reduce the required processing power and still allow for multimodal or non-Gaussian uncertain distributions, we propose a scheme based on a particle filter and a corresponding cloud of control signals. The approach avoids the use of the certainty equivalence principle by postponing the decision on the optimal estimate to the control stage. As the mapping between the pose space and the control action space is nonlinear and the best estimation of robot pose is uncertain, postponing the decision to the control space makes it possible to select a better control action in the presence of multimodal and non-Gaussian uncertainty models. Simulation results are presented.

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Section: Control Using a Cloud Of Particlesmentioning

confidence: 99%

Section: Control Using a Cloud Of Particlesmentioning

confidence: 99%