Modular approach to compact low-speed kinematic modelling of multi-articulated urban buses for motion algorithmization purposes

Michałek, Maciej Marcin

doi:10.1109/ivs.2019.8814281

Cited by 13 publications

(22 citation statements)

References 13 publications

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“…In order to describe a configuration of the N-trailer let us introduce the following variables (see [25]):…”

Section: A Configuration Variables Inputs and Basic Relationshipsmentioning

confidence: 99%

“…Using the above basic relationships, one can derive a nonholonomic kinematic model of the N-trailer in the form of driftless dynamics (see [21], [25] for more details)…”

Section: A Configuration Variables Inputs and Basic Relationshipsmentioning

confidence: 99%

“…A maneuverability analysis seems to be especially important in view of various motion tasks involving agile maneuvering with a trailer [24]. Preserving or shaping a maneuverability is [19], [25] (note: VSW is not a physical wheel).…”

Section: Introductionmentioning

confidence: 99%

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Trailer-Maneuverability in N-Trailer Structures

Michałek

2020

IEEE Robot. Autom. Lett.

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The letter presents a trailer-maneuverability analysis for the N-trailer kinematic structures comprising a tractor and an arbitrary number of single-axle trailers equipped with steerable or fixed wheels. For an elliptically-shaped set of tractor's inputvelocities, we derive a mobility-ellipse generated in a velocity space of the (last) trailer. We show that the on-axle type of hitching present in the N-trailer leads to a singularity of a trailer-mobility (at least one degree of mobility is lost). Furthermore, we provide some relations between kinematic parameters of the N-trailer and its control inputs which enable shaping the trailer-mobility ellipse and decoupling it from a joint-configuration of the vehicle.

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“…In order to describe a configuration of the N-trailer let us introduce the following variables (see [25]):…”

Section: A Configuration Variables Inputs and Basic Relationshipsmentioning

confidence: 99%

“…Using the above basic relationships, one can derive a nonholonomic kinematic model of the N-trailer in the form of driftless dynamics (see [21], [25] for more details)…”

Section: A Configuration Variables Inputs and Basic Relationshipsmentioning

confidence: 99%

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Trailer-Maneuverability in N-Trailer Structures

Michałek

2020

IEEE Robot. Autom. Lett.

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“…The mathematical optimization approaches, firstly formulating a cost function of the automatic parking problem and then minimizing the objective cost function [9][10][11][12][13]. Besides car-like vehicles, reference trajectory is feasible in the case of parking N-trailer vehicles when complicated kinematic constraints are taken into account in planning the trajectory [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…in the case of parking N-trailer vehicles when complicated kinematic constraints are taken into account in planning the trajectory [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Inverse Vehicle Model for a Neural-Network-Based Integrated Lateral and Longitudinal Automatic Parking Controller

2019

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The majority of currently used automatic parking systems exploit the planning-and-tracking approach that involves planning the reference trajectory first and then tracking the desired reference trajectory. However, the response delay of longitudinal velocity prevents the parking controller from tracing the desired trajectory because the vehicle's velocity and other state parameters are not synchronized, while the controller maneuvers the vehicle according to the planned desired velocity and steering profiles. We propose an inverse vehicle model to provide a neural-network-based integrated lateral and longitudinal automatic parking controller. We approximated the relationship of the planned velocity to the vehicle's velocity using a second-order difference equation that involves the response characteristic of the vehicle's longitudinal delay. The adjusted desired velocity to track the origin-planned velocity is calculated using the inverse vehicle model. Furthermore, we proposed an integrated longitudinal and lateral parking controller using an artificial neural network (ANN) model trained on a dataset applying the inverse vehicle model. By learning the control laws between the vehicle's states and the corresponding actions, the proposed ANN-based controller could yield a steering angle and the adjusted desired velocity to complete automatic parking in a confined space.Fuzzy-based and knowledge-based approaches have been presented [17][18][19]. These methods are capable of providing solutions within a range of designed rules with an advantage in easy implements and practical usages.Recently, methods based on deep neural networks have been expected to solve the drawbacks of the mentioned step-by-step approaches by maneuvering vehicles without prior offline trajectory planning [20][21][22][23]. By training an artificial neural network (ANN) using a dataset generated by simulation or experiment, the ANN learns hyper-dimensional relationships between the current vehicle states and the appropriate vehicle maneuvering signals. Instead of calculating the parking trajectory offline, the ANN-based parking controller can yield a direct maneuvering signal of the steering angle and velocity online, while the vehicle is moving into a parking space. Liu et al. presented a method to enumerate all the possible parking trajectories and corresponding steering actions, and then have the parking controller learn the relationship between the given initial-and-final state pairs and the corresponding sequence of steering actions using an ANN [20]. Li et al. proposed an end-to-end neural-network-based automatic parking controller [21]. Rathour also proposed an encoder-decoder architecture for automatic parking [22]. Moon et al. developed an automatic parking controller with a twin ANN architectures [23].However, neither the planning-based methods nor ANN-based controllers have taken account of longitudinal control delay for a vehicle under automatic parking. Figure 1 shows a typical block diagram of a conventional automatic parking contr...

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Lining-Up Stabilizers for Pusher and Puller Articulated Vehicles

Michałek

2020

Advances in Intelligent Systems and Computing

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Modular approach to compact low-speed kinematic modelling of multi-articulated urban buses for motion algorithmization purposes

Cited by 13 publications

References 13 publications

Trailer-Maneuverability in N-Trailer Structures

Trailer-Maneuverability in N-Trailer Structures

An Inverse Vehicle Model for a Neural-Network-Based Integrated Lateral and Longitudinal Automatic Parking Controller

Lining-Up Stabilizers for Pusher and Puller Articulated Vehicles

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