Vehicle modeling and parameter estimation using adaptive limited memory joint-state UKF

You, Changxi; Tsiotras, Panagiotis

doi:10.23919/acc.2017.7962973

Cited by 14 publications

(8 citation statements)

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“…The ALM-UKF, introduced in [28], is an estimation algorithm for nonlinear systems that builds on previous works on UKF to simultaneously estimate the system state, model parameters, and the Kalman filter hyperparameters related to the noise. First, recall that the adaptive Kalman filter algorithm [29] adjusts the mean and the covariance of the noise online, which is expected to compensate for time-varying modeling errors.…”

Section: Adaptive Limited Memory Unscented Kalman Filtermentioning

confidence: 99%

“…All samples r k and q k are assumed to be statistically independent and identically distributed. The summarized algorithm of the ALM-UKF based on equations ( 12)-( 26) is presented in [28], where α, β, κ and λ are the UT parameters [27].…”

Section: Adaptive Limited Memory Unscented Kalman Filtermentioning

confidence: 99%

“…We thus only show the estimation results of the unknown vehicle parameters. We implemented the ALM-JUKF [28] to estimate the parameters of a full vehicle model from (5a)-(7c) using the AutoRally experimental data, which includes the measurements for V x , V y , V z , r,θ,φ, ψ, θ, φ…”

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confidence: 99%

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AutoRally: An Open Platform for Aggressive Autonomous Driving

et al. 2019

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Scaled platforms constructed from modified RC cars are popular in the academic and hobby communities. These platforms are typically 0.2 m to 1 m long and weigh between 1 kg and 25 kg. Costs range from a few hundred to tens of thousands of dollars, largely determined by the size, sensors, and computing. Construction, maintenance, and programming is typically handled by a small team of students or researchers. Recently, several open source projects released complete documentation and interface software, which is in contrast to the one-off nature of older work that often lacked enough information to replicate. Documentation for open source platforms normally includes parts lists, build instructions, 3 and interface software for the sensors and actuators. Availability of tutorials, simulation environments, and public datasets vary by project. Common sensors include wheel speed, inertial measurement unit (IMU), cameras, depth sensors, ultrasonic, and light detection and ranging (Lidar) units. The target environment for these platforms is typically indoors on a smooth surface. The Donkey Car [5] is an easy to build 1:16 scale autonomous platform for the DIY Roborace events targeted at hobbyists. Onboard computing and sensing are a Raspberry Pi 3 with a matching wide angle camera. The Berkeley Autonomous Race Car (BARC) [6] is a 1:10 scale vehicle designed as a simple and affordable research platform for self-driving vehicle technologies that has been successfully used to demonstrate various control algorithms. The onboard ODROID-XU4 is similar in computational performance to the Raspberry Pi 3, and the sensor suite includes a hobby grade camera, IMU, four ultrasonic range finders, and Hall effect wheel speed sensors. The F1/10 project [7] and accompanying Autonomous Racing Competition allows teams to race against one another using a common 1:10 scale platform developed at the University of Pennsylvania. Computing on the F1/10 platform is performed by an Nvidia Jetson. The sensor suite includes a hobby IMU, compact indoor Hokuyo 2D Lidar, and optional Structure and Zed depth and motion sensing cameras. The 1:10 scale Rapid Autonomous Complex-Environment Competing Ackermann-steering Robot (RACECAR) [8] from Massachusetts Institute of Technology is a platform for researchers creating applications for self driving cars. RACECAR also uses the Nvidia Jetson for computing, and includes the same Hokuyo Lidar and Zed stereo camera as the F1/10 platform. Table 1 provides a comparison of these open source scaled platforms.

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Section: Adaptive Limited Memory Unscented Kalman Filtermentioning

confidence: 99%

Section: Adaptive Limited Memory Unscented Kalman Filtermentioning

confidence: 99%

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AutoRally: An Open Platform for Aggressive Autonomous Driving

et al. 2019

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“…In this paper, we show that by considering the vertical dynamics of the vehicle which have been neglected in previous works, we can gain a more complete understanding of the vehicle's motion and interaction with the terrain. We extend previous efforts at online estimation of terrain parameters such as in [2], [4], [5], by investigating the This work was supported by the Automotive Research Center (ARC), a US Army Center of Excellence for modeling and simulation of ground vehicles, under Cooperative Agreement W56HZV-19-2-0001 with the US Army DEVCOM Ground Vehicle Systems Center (GVSC).…”

Section: Introductionmentioning

confidence: 99%

“…In [2], it was shown that parameters of the Bekker model can be estimated using a UKF, but the only vehicle model considered there was a dynamic bicycle with vertical dynamics neglected and the terrain was considered level. In [5], a UKF was also used to estimate vehicle and terrain parameters and vertical vehicle dynamics were considered, but the terrain was assumed to be level and non-deformable, with the terrain interaction being modelled by Pacejka's 'magic formula' with unknown parameters. In [8], it was shown that variations in the terrain, specifically in the parameters of the Bekker model, can lead to significant variations in the vertical vehicle dynamics as the vehicle traverses an uneven terrain.…”

Section: Introductionmentioning

confidence: 99%

Terrain parameter estimation from proprioceptive sensing of the suspension dynamics in offroad vehicles

Buzhardt¹,

Tallapragada²

2022

Preprint

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Offroad vehicle movement has to contend with uneven and uncertain terrain which present challenges to path planning and motion control for both manned and unmanned ground vehicles. Knowledge of terrain properties can allow a vehicle to adapt its control and motion planning algorithms. Terrain properties, however, can change on time scales of days or even hours, necessitating their online estimation. The kinematics and, in particular the oscillations experienced by an offroad vehicle carry a signature of the terrain properties. These terrain properties can thus be estimated from proprioceptive sensing of the vehicle dynamics with an appropriate model and estimation algorithm. In this paper, we show that knowledge of the vertical dynamics of a vehicle due to its suspension can enable faster and more accurate estimation of terrain parameters. The paper considers a five degree of freedom model that combines the well known half-car and bicycle models. We show through simulation that the sinkage exponent, a parameter that can significantly influence the wheel forces from the terrain and thus greatly impact the vehicle trajectory, can be estimated from measurements of the vehicle's linear acceleration and rotational velocity, which can be readily obtained from an onboard IMU . We show that modelling the vertical vehicle dynamics can lead to significant improvement in both the estimation of terrain parameters and the prediction of the vehicle trajectory.

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