Online slip parameter estimation for tracked vehicle odometry on loose slope

Yamauchi, Genki; Suzuki, Daiki; Nagatani, Keiji

doi:10.1109/ssrr.2016.7784303

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…The loss function is mse. X r , Y r is a random point from a 30 cm 2 region, which represents the position of the Wheeled Mobile Robot (WMR), X m , Y m is current weighted mean of the PF, while [0][1][2][3][4][5][6][7] is the output of the DQN. γ is 0.95, exploration-rate is 1.0, exploration-minimum is 0.01, exploration-decay is 0.995, sample batch size is 100 and number of episodes are 10,000.…”

Section: Dqn Parametersmentioning

confidence: 99%

“…So, to bridge a gap between planning and navigation, WMR slip estimation is required in order to take corrective measures afterward. There are many slip-estimation mechanisms to improve the localization, navigation, and tracking performance [3][4][5][6][7]. Inertial Measurement Unit (IMU)-based localization and slip estimation [8], fuzzy-controller and current-sensing based slip estimation [9,10], Kalman and Particle Filter based slip estimation [11,12], and vision-based slip estimation [13].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

Ullah

Zhang

et al. 2018

Symmetry

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Abstract:A novel approach is proposed for the path tracking of a Wheeled Mobile Robot (WMR) in the presence of an unknown lateral slip. Much of the existing work has assumed pure rolling conditions between the wheel and ground. Under the pure rolling conditions, the wheels of a WMR are supposed to roll without slipping. Complex wheel-ground interactions, acceleration and steering system noise are the factors which cause WMR wheel slip. A basic research problem in this context is localization and slip estimation of WMR from a stream of noisy sensors data when the robot is moving on a slippery surface, or moving at a high speed. DecaWave based ranging system and Particle Filter (PF) are good candidates to estimate the location of WMR indoors and outdoors. Unfortunately, wheel-slip of WMR limits the ultimate performance that can be achieved by real-world implementation of the PF, because location estimation systems typically partially rely on the robot heading. A small error in the WMR heading leads to a large error in location estimation of the PF because of its cumulative nature. In order to enhance the tracking and localization performance of the PF in the environments where the main reason for an error in the PF location estimation is angular noise, two methods were used for heading estimation of the WMR (1): Reinforcement Learning (RL) and (2): Location-based Heading Estimation (LHE). Trilateration is applied to DecaWave based ranging system for calculating the probable location of WMR, this noisy location along with PF current mean is used to estimate the WMR heading by using the above two methods. Beside the WMR location calculation, DecaWave based ranging system is also used to update the PF weights. The localization and tracking performance of the PF is significantly improved through incorporating heading error in localization by applying RL and LHE. Desired trajectory information is then used to develop an algorithm for extracting the lateral slip along X-and Y-axis from the PF estimated position of the WMR, the lateral slip along X-and Y-axis is then used to take some corrective measures. Lateral slip information is also used to find the direction along which WMR has to move to get back along the desired trajectory. Simulation results show that our proposed LHE and RL heading estimation methods significantly improve the PF localization and tracking performance on a slippery surface in both indoor and outdoor environments. The simulation results also show that the accurate locations of WMR and desired path information are used to estimate and compensate the lateral slip.

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Section: Dqn Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

Ullah

Zhang

et al. 2018

Symmetry

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show abstract

“…The accuracy of the slippage mode observer was verified in accordance with both simulation and experimental results. Yamauchi et al [19] built a slip estimation method using a slippage model for tracked vehicles and applied it to slipcompensated odometry on Loose Slope. Their method was validated through an indoor slope-traveling experiment.…”

Section: Introductionmentioning

confidence: 99%

A New Model to Predict the Slippage Coefficient of Tracked Vehicles During Steering

Zhao

Wang

et al. 2022

IEEE Access

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Tracked vehicles during steering are always subjected to track slippage relative to the ground. Studies show that an excessive slippage coefficient reduces the steering ability of tracked vehicles and increases the risk of soil shear failure. Accordingly, fast and accurate tests of the slippage coefficient of tracked vehicles under different ground conditions can be effectively referenced to select track parameters and reach optimal operation. Aiming to resolve shortcomings of the conventional testing method in determining the slippage coefficient of tracked vehicles, a novel method is proposed in the present study. Then the trajectory of an arbitrary point on the tracked vehicle during steering was analyzed. Moreover, the time of vehicles steering around and the speed of track sprocket were measured, and the slippage coefficient of tracked vehicles steering on the specific ground condition was determined. Meanwhile, the slippage coefficient of the tracked combined harvester steering under different ground conditions was explored using the conventional method. The obtained results were compared quantitatively, and the accuracy of the established model was verified. The performed analyses demonstrate that the proposed method has a simple structure and can be effectively applied to various complicated working conditions. This method is expected to lay a theoretical basis to establish an online testing system for the slippage coefficient of tracked vehicles during steering.

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“…It is proved that the wheel slip estimation is challenging but important in the current generation of autonomous applications [7]. For example, wheel encoders may generate a large error in velocity estimation because of the slip, which will cause failure in traversing a given path [8]. Thus, wheel slip should be considered in the current sensor fusion methods.…”

Section: Introductionmentioning

confidence: 99%

Rao‐Blackwellised particle filtering for low‐cost encoder/INS/GNSS integrated vehicle navigation with wheel slipping

Zhou

Yao

Fan

et al. 2019

IET Radar, Sonar & Navigation

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Online slip parameter estimation for tracked vehicle odometry on loose slope

Cited by 13 publications

References 12 publications

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

A New Model to Predict the Slippage Coefficient of Tracked Vehicles During Steering

Rao‐Blackwellised particle filtering for low‐cost encoder/INS/GNSS integrated vehicle navigation with wheel slipping

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