Path tracking of a four-wheel steering mobile robot: A robust off-road parallel steering strategy

Deremetz, Mathieu; Lenain, Roland; Couvent, Adrian; Cariou, Christophe; Thuilot, Benoı̂t

doi:10.1109/ecmr.2017.8098670

Cited by 11 publications

(10 citation statements)

References 15 publications

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“…This fact is common to the other driver models such as Stanley method and PID control [31]. The error dynamics based path tracking controller can be used for a vehicle with 4WS because the front and rear steering angles are explicitly expressed as a control input of the controllers such as LQR and MPC [33][34][35][36][37][38][39][40]. However, in this case, assumptions on a target path are required to build the error dynamics.…”

Section: A Pure Pursuit Methods For Steering Angle Generationmentioning

confidence: 99%

“…Typical driver model for path tracking control is pure pursuit and Stanley methods. Another method is to use an error dynamics on a target path and to apply optimal control methodologies or sliding mode control with it for controller design [33][34][35][36][37][38][39][40]. In the method, the errors of the lateral offset and heading are used to define the plant model for controller design.…”

Section: Derivation Of Reference Yaw Ratementioning

confidence: 99%

“…Recent advances in path tracking control were summarized in the literature [27][28][29][30][31][32]. Among them, the path tracking control was investigated for a vehicle with 4WS or 4WD [4,9,16,[33][34][35][36][37][38][39][40]. The recent studies on the path tracking control with 4WS and 4WD are summarized in Table I.…”

Section: Introductionmentioning

confidence: 99%

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Path Tracking Control With Four-Wheel Independent Steering, Driving and Braking Systems for Autonomous Electric Vehicles

Jeong

Yim

2022

IEEE Access

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This paper presents a method to design a path tracking controller with four-wheel independent braking (4WIB), drive (4WID) and steering (4WIS) systems equipped in in-wheel motordriven electric vehicles (IWM-EVs). Generally, it is difficult to calculate the steering angles of 4WIS and the braking/traction torques of 4WIB/4WID for path tracking control. Moreover, there have been limitations of an error dynamics-based path tracking controller, which requires assumptions on a target path. To cope with these problems, the path tracking problem on a target path is converted into the yaw rate tracking one with a reference yaw rate in this paper. Two methods are adopted for the purpose of calculating a reference yaw rate. The first is to use a pure pursuit method, which generates a steering angle for path tracking. From the steering angle, a reference yaw rate is calculated. The second is to derive a reference yaw rate from a target path and a current vehicle position. For yaw rate tracking, direct yaw moment control is adopted to generate a control yaw moment. A control allocation method is adopted to distribute a control yaw moment into tire forces, generated by 4WIS, 4WID and 4WIB. Several actuator combinations are represented by various sets of virtual weights in the control allocation method. A simulation with a vehicle simulation program, CarSim ® , shows that the proposed path tracking controller is effective in enhancing the path tracking performance with 4WIS, 4WID and 4WIB. From the simulation, effects of actuator combinations on path tracking performance are analyzed.INDEX TERMS Path tracking control, Yaw rate tracking control, 4-wheel independent steering (4WIS), 4wheel independent drive (4WID), 4-wheel independent braking (4WIB), In-wheel motor (IWM) system

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Section: A Pure Pursuit Methods For Steering Angle Generationmentioning

confidence: 99%

Section: Derivation Of Reference Yaw Ratementioning

confidence: 99%

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Path Tracking Control With Four-Wheel Independent Steering, Driving and Braking Systems for Autonomous Electric Vehicles

Jeong

Yim

2022

IEEE Access

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“…Although non-slip hypothesis is widely assumed in this literature, the extended kinematic model enables considering the slip of the vehicle in the off-road context. [12]. The dynamic bicycle model also considers the slip of the vehicle, even when both front and rear wheels are steerable [13].…”

Section: Introductionmentioning

confidence: 99%

Multivariable lateral control of an off-road vehicle operating on sloping grounds

Legrand

Claveau

Chevrel

et al. 2022

2022 European Control Conference (ECC)

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This paper proposes an optimized lateral control design of a two-steering-axle off-road vehicle. An extension of the well-known bicycle model is introduced that considers two steering axles and the slope of the local ground. Lateral deviation controllers are thereby synthesized to quantitatively evaluate the use of multiple steering angles. The proposed multi-input multi-output feedback controllers originate from a H2/Hinf multi-objective synthesis, achieving optimum tradeoffs between performance and robustness. Three controller architectures are considered (depending on actuation possibilities) and compared qualitatively and quantitatively. In addition, realistic simulation results are analyzed using a non-linear simulator that considers the fine modeling of sensors and actuators (e.g., hydraulic actuator). The results are promising and will be the subject of an experimental validation project.

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“…For localization, there are also efforts using visual techniques [28,29] and estimation techniques [30,31]. After obtaining the orientation angle data, the next step is to control the orientation of the robot to ensure the robustness and resilience of the overall system [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Orientation Tracking for a Façade-Cleaning Robot

Vega-Heredia

Ilyas

Ghanta

et al. 2020

Sensors

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Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots.

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Path tracking of a four-wheel steering mobile robot: A robust off-road parallel steering strategy

Cited by 11 publications

References 15 publications

Path Tracking Control With Four-Wheel Independent Steering, Driving and Braking Systems for Autonomous Electric Vehicles

Path Tracking Control With Four-Wheel Independent Steering, Driving and Braking Systems for Autonomous Electric Vehicles

Multivariable lateral control of an off-road vehicle operating on sloping grounds

Multi-Sensor Orientation Tracking for a Façade-Cleaning Robot

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