Optimal Coordination of ADAS and Chassis Systems with Different Time-Delays

Kissai, Moad; Do, Anh-Lam; Monsuez, Bruno; Mouton, Xavier; Tapus, Adriana

doi:10.1109/iv47402.2020.9304797

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The field o f r obotics i s c ontinually a dvancing t owards m ore a utonomous a nd r obust s ystems, 1 driven by rapid enhancements in sensor technology and intelligent algorithms. 2 Among the sensors employed, the Inertial Measurement Unit (IMU) is fundamental for providing vital motion data. However, a significant challenge that persists is the IMU drift -an inherent error accumulation over time that can severely skew the state estimation of robots, leading to degraded operational accuracy and potential system malfunctions.…”

Section: Introduction 11 Motivationmentioning

confidence: 99%

Enhanced robot state estimation using physics-informed neural networks and multimodal proprioceptive data

Liu,

Bao,

Cheng

et al. 2024

Sensors and Systems for Space Applications XVII

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In this study, we introduce an innovative Robot State Estimation (RSE) methodology incorporating a learning-based contact estimation framework for legged robots, which obviates the need for external physical contact sensors. This approach integrates multimodal proprioceptive sensory data, employing a Physics-Informed Neural Network (PINN) in conjunction with an Unscented Kalman Filter (UKF) to enhance the state estimation process. The primary objective of this RSE technique is to calibrate the Inertial Measurement Unit (IMU) effectively a nd f urnish a d etailed r epresentation o f t he r obot's d ynamic state.Our methodology exploits the PINN to mitigate IMU drift issues by imposing constraints on the loss function via Ordinary Differential E quations ( ODEs). T he a dvantages o f u tilizing a c ontact e stimator b ased on proprioceptive sensory data are multifold. Unlike vision-based state estimators, our proprioceptive approach is immune to visual impairments such as obscured or ambiguous environments. Moreover, it circumvents the necessity for dedicated contact sensors-components not universally present on robotic platforms and challenging to integrate without substantial hardware modifications.The contact estimator within our network is trained to discern contact events across various terrains, thereby facilitating resilient proprioceptive odometry. This enables the contact-aided invariant Kalman Filter to produce precise odometric trajectories. Subsequently, the UKF algorithm estimates the robot's three-dimensional attitude, velocity, and position.Experimental validation of our proposed PINN-based method illustrates its capacity to assimilate data from multiple sensors, effectively r educing t he i nfluence of se nsor bi ases by en forcing OD E co nstraints, all while preserving intrinsic sensor characteristics. When juxtaposed with the employment of the UKF algorithm in isolation, our integrated RSE model demonstrates superior performance in state estimation. This enhanced capability automatically reduces sensor drift impacts during operational deployment, rendering our proposed solution applicable to real-world scenarios.

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Section: Introduction 11 Motivationmentioning

confidence: 99%

Enhanced robot state estimation using physics-informed neural networks and multimodal proprioceptive data

Liu,

Bao,

Cheng

et al. 2024

Sensors and Systems for Space Applications XVII

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show abstract

“…These subsystems improve specific vehicle performance, making driving more efficient and effective. Vehicle chassis subsystems not only make driving more convenient [3], but they also make the vehicle a complex autonomous system. The optimal coordination of chassis system (OCCS) [4] is applied to coordinate different complementary chassis subsystems.…”

Section: Introductionmentioning

confidence: 99%

Vehicle State Estimation Combining Physics-Informed Neural Network and Unscented Kalman Filtering on Manifolds

Tan

Cai

Wang

et al. 2023

Sensors

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This paper proposes a novel vehicle state estimation (VSE) method that combines a physics-informed neural network (PINN) and an unscented Kalman filter on manifolds (UKF-M). This VSE aimed to achieve inertial measurement unit (IMU) calibration and provide comprehensive information on the vehicle’s dynamic state. The proposed method leverages a PINN to eliminate IMU drift by constraining the loss function with ordinary differential equations (ODEs). Then, the UKF-M is used to estimate the 3D attitude, velocity, and position of the vehicle more accurately using a six-degrees-of-freedom vehicle model. Experimental results demonstrate that the proposed PINN method can learn from multiple sensors and reduce the impact of sensor biases by constraining the ODEs without affecting the sensor characteristics. Compared to the UKF-M algorithm alone, our VSE can better estimate vehicle states. The proposed method has the potential to automatically reduce the impact of sensor drift during vehicle operation, making it more suitable for real-world applications.

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Optimal Coordination of ADAS and Chassis Systems with Different Time-Delays

Cited by 2 publications

References 17 publications

Enhanced robot state estimation using physics-informed neural networks and multimodal proprioceptive data

Enhanced robot state estimation using physics-informed neural networks and multimodal proprioceptive data

Vehicle State Estimation Combining Physics-Informed Neural Network and Unscented Kalman Filtering on Manifolds

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