Optimal kinematic path tracking control of mobile robots with front steering

Hemami, Ahmad; Mehrabi, M.G.; Cheng, R. M. H.

doi:10.1017/s0263574700016908

Cited by 9 publications

(8 citation statements)

References 12 publications

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“…The control problem examined in this paper is a path tracking problem, which was formulated in [5] for a class of low speed tricycle-model vehicles. Essentially, the control objective is to successfully navigate a mobile robot along a desired path in a two-dimensional environment.…”

Section: Mobile Robot Path Trackingmentioning

confidence: 99%

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Design of fuzzy logic controllers for genetic programming

Ren

2005

SPIE Proceedings

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Genetic programming (GP) is applied to the design of fuzzy logic controllers (FLCs) for mobile robot path tracking. GP is applied to automatic discovery of full knowledge bases for use in fuzzy logic control applications. An extension to a rule learning GP system is presented that achieves this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to design a mobile robot path tracking controller and performance is shown to be comparable to that of a manually designed controller. GP was successfully applied to discover FLCs capable of steering a mobile robot to track straight-line paths in the plane. Instances of simultaneous evolution of membership functions and rules showed that GP was capable of evolving a FLC that demonstrated satisfactory responsiveness to various initial conditions while utilizing minimal human interface.

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Section: Mobile Robot Path Trackingmentioning

confidence: 99%

“…The orientation error is the angular deviation of the robot from the tangent of the desired path. Hemami [5] et al derived a state-space kinematic model for this robot where the state vector was comprised of the pose errors described which culminates in the following:…”

Section: Mobile Robot Path Trackingmentioning

confidence: 99%

Design of fuzzy logic controllers for genetic programming

Ren

2005

SPIE Proceedings

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“…Existing tracking methods concretely include PID control [11], fuzzy control [12,13], optimal control [14], pure pursuit control [15], neural network control [16,17], and so on. They improve the goal points tracking performance for automatic vehicles in different aspects.…”

Section: Introductionmentioning

confidence: 99%

“…It is worth mentioning that this method is easy to implement due to the use of linear control techniques. Reference [14] requires the vehicle to run at low speed, so the dynamic properties have little influence on the path tracking behavior. In [14], an optimal controller is designed to minimize the orientation error and the offset from a path.…”

Section: Introductionmentioning

confidence: 99%

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Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network

2019

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In order to efficiently and exactly in tracking the desired path points, autonomous cleaning vehicles have to adapt their own behavior according to the perceived environmental information. This paper proposes a target points tracking control algorithm based on the Long Short-Term Memory network, which can generate the speed and yaw rate to arrive at the target point in real time. The target point is obtained by a parameter named foresight distance that is deduced based on the fuzzy control, whose inputs are the speed and yaw rate of the vehicle at the current point. The effectiveness of the proposed algorithm is illustrated by the simulation and field experiments. Compared with other classical algorithms, this algorithm can track the point sequence on straight path and multiple curvature path more accurately. The field experiment indicates the proposed controller is efficient in following the pre-defined path points, furthermore, it can make the autonomous cleaning vehicle run smoothly in the path which is disturbed by bounded disturbances. The distance errors can meet the actual requirement of the cleaning vehicle during the tracking process.

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Mixing input-output pseudolinearization and gain scheduling techniques for stabilization of mobile robots with two independently driven wheels

1997

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In this paper, we propose a two-loop structure to transform and stabilize the kinematic model of a nonholonomic mobile robot with two independently driven wheels. This two-loop structure consists of input-output pseudolinearization and gain scheduling techniques. A comparison with previous methods is included. The main contribution of this paper is to apply a input-output pseudolinearization transformation method and to use an effective pole-assignment strategy for stabilizing a mobile robot with two independently driven wheels. The proposed method has demonstrated superiority over previous methods.

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Optimal kinematic path tracking control of mobile robots with front steering

Cited by 9 publications

References 12 publications

Design of fuzzy logic controllers for genetic programming

Design of fuzzy logic controllers for genetic programming

Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network

Mixing input-output pseudolinearization and gain scheduling techniques for stabilization of mobile robots with two independently driven wheels

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