Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment

Zhang, Xing; Yuan, Shuai; Yin, Xiuxing; Li, Xueyuan; Qu, Xinyi; Liu, Qi

doi:10.3390/app112110391

Cited by 4 publications

(2 citation statements)

References 42 publications

(50 reference statements)

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“…Currently, mobile robotic vehicles are either without suspension or have suspension consisting of a system of springs and shock absorbers [15][16][17][18]. In this article, we will try to design the suspension based on a classic leaf spring.…”

Section: Introductionmentioning

confidence: 99%

Measurement of a Vibration on a Robotic Vehicle

Klimenda

Cizek

Suszyński

2022

Sensors

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This article deals with the design and construction of a robotic vehicle. The first part of the paper focuses on the selection of suitable variants for the robotic vehicle arrangement, i.e., frame, electric motors with gearboxes, wheels, steering and accumulators. Based on the selection of individual components, the robotic vehicle was built. An important part of the robotic vehicle was the design of the suspension of the front wheels. The resulting shape of the springs was experimentally developed from several design variants and subsequently produced by an additive manufacturing process. The last part of article is devoted to the experimental measurement of the acceleration transfer to the upper part of the frame during the passage of the robotic vehicle over differently arranged obstacles. Experimental measurements measured the accelerations that are transferred to the top of the robotic vehicle frame when the front wheels of the vehicle cross over the obstacle (obstacles). The maximum acceleration values are 0.0588 m/s2 in the x-axis, 0.0149 m/s2 in the y-axis and 0.5755 m/s2 in the z-axis. This experimental solution verifies the stiffness of the designed frame and the damping effect of the selected material of the designed springs on the front wheels of the robotic vehicle.

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Section: Introductionmentioning

confidence: 99%

Measurement of a Vibration on a Robotic Vehicle

Klimenda

Cizek

Suszyński

2022

Sensors

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“…In the UKF, the proper positioning of the sigma points is an essential process for reducing the UT approximation error in dynamic situations. Various algorithms of the UKF have been researched on adjusting set volumes according to system dynamics with the fixed scaling parameter in the sigma point [ 16 , 17 , 18 , 19 , 20 ]. The authors proposed the adaptive UKF attempts to adaptively estimate the means and covariances of both process and measurement noises in the paper [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Regularized Optimal Transport Based on an Adaptive Adjustment Method for Selecting the Scaling Parameters of Unscented Kalman Filters

Kang

Kim

2022

Sensors

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In this paper, an adaptation method for adjusting the scaling parameters of an unscented Kalman filter (UKF) is proposed to improve the estimation performance of the filter in dynamic conditions. The proposed adaptation method is based on a sequential algorithm that selects the scaling parameter using the user-defined distribution of discrete sets to more effectively deal with the changing measurement distribution over time and avoid the additional process for training a filter model. The adaptation method employs regularized optimal transport (ROT), which compensates for the error of the predicted measurement with the current measurement values to select the proper scaling parameter. In addition, the Sinkhorn–Knopp algorithm is used to minimize the cost function of ROT due to its fast convergence rate, and the convergence of the proposed ROT-based adaptive adjustment method is also analyzed. According to the analysis results of Monte Carlo simulations, it is confirmed that the proposed algorithm shows better performance than the conventional algorithms in terms of the scaling parameter selection in the UKF.

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Research on Tire–Road Parameters Estimation Algorithm for Skid-Steered Wheeled Unmanned Ground Vehicle

Zhu

Zhang

et al. 2022

Machines

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Skid-steered wheeled vehicles can be applied in military, agricultural, and other fields because of their flexible layout structure and strong passability. The research and application of vehicles are developing towards the direction of “intelligent” and “unmanned”. As essential parts of unmanned vehicles, the motion planning and control systems are increasingly demanding for model and road parameters. In this paper, an estimation method for tire and road parameters is proposed by combining offline and online identification. Firstly, a 3-DOF nonlinear dynamic model is established, and the interaction between tire and road is described by the Brush nonlinear tire model. Then, the horizontal and longitudinal stiffness of the tire is identified offline using the particle swarm optimization (PSO) algorithm with adaptive inertia weight. Referring to the Burckhardt adhesion coefficient formula, the extended forgetting factor recursive least-squares (EFRLS) method is applied to identify the road adhesion coefficient online. Finally, the validity of the proposed identification algorithm is verified by TruckSim simulation and real vehicle tests. Results show that the relative error of the proposed algorithm can be well controlled within 5%.

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Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment

Cited by 4 publications

References 42 publications

Measurement of a Vibration on a Robotic Vehicle

Measurement of a Vibration on a Robotic Vehicle

Regularized Optimal Transport Based on an Adaptive Adjustment Method for Selecting the Scaling Parameters of Unscented Kalman Filters

Research on Tire–Road Parameters Estimation Algorithm for Skid-Steered Wheeled Unmanned Ground Vehicle

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