Fuzzy logic approaches for wheeled skid-steer vehicles

Economou, J.T.; Colyer, R.E.; Tsourdos, Antonios; White, Brian

doi:10.1109/vetecf.2002.1040749

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…However, it does not have strong traction and mobility over rough and loose terrain and, hence, is seldom used for outdoor terrains. Like differential steering, skid steering leads to high maneuverability [2], [6], [7] and faster response [8] and has a simple [3], [4], [9] and robust mechanical structure [4], [10], [11]. In contrast, it also leads to strong traction and high mobility [9], which makes it suitable for all-terrain traversal.…”

Section: Analysis and Experimental Verification For Dynamicmentioning

confidence: 99%

“…The most thorough dynamic analysis of a skid-steered vehicle is found in [16] and [21], which consider steady-state (i.e., constant linear and angular velocities) dynamic models for circular motion of tracked vehicles. A primary contribution of this research is that it proposes and then provides experimental evidence that in the track-terrain interaction, the shear stress is a particular function of the shear displacement (see (7) of Section III). This model differs from the Coulomb model of friction, adopted in [6] and [11], which essentially assumes that the maximum shear stress is obtained as soon as there is any relative movement between the track and the ground.…”

Section: Analysis and Experimental Verification For Dynamicmentioning

confidence: 99%

“…The inputs to (8)- (16), shown below, are the left and right wheel angular velocities ω l and ω r . We begin by describing the relationship between the shear stress and shear displacement associated with the sliding of the wheel over the terrain [see (7), shown below]. Then, the constant angular velocities for the left and right wheels (ω l and ω r ) are used to determine the wheel sliding velocities [see (9) and (11), shown below].…”

Section: A 2-d General Motionmentioning

confidence: 99%

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Analysis and Experimental Verification for Dynamic Modeling of A Skid-Steered Wheeled Vehicle

Chuy

Collins

et al. 2010

IEEE Trans. Robot.

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Skid-steered vehicles are often used as outdoor mobile robots due to their robust mechanical structure and high maneuverability. Sliding, along with rolling, is inherent to general curvilinear motion, which makes both kinematic and dynamic modeling difficult. For the purpose of motion planning, this paper develops and experimentally verifies dynamic models of a skid-steered wheeled vehicle for general planar (2-D) motion and for linear 3-D motion. These models are characterized by the coefficient of rolling resistance, the coefficient of friction, and the shear deformation modulus, which have terrain-dependent values. The dynamic models also include motor saturation and motor power limitations, which enable correct prediction of vehicle velocities when traversing on hills. It is shown that the closed-loop system that results from inclusion of the dynamics of the [proportional-integral-derivative (PID)] speed controllers for each set of wheels does a much better job than the open-loop model of predicting the vehicle linear and angular velocities. For a vehicle turning with small linear and angular accelerations, the model provides accurate predictions of velocities and reasonable predictions of torques. Hence, the closedloop model is recommended for motion planning.

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Section: Analysis and Experimental Verification For Dynamicmentioning

confidence: 99%

Section: Analysis and Experimental Verification For Dynamicmentioning

confidence: 99%

Section: A 2-d General Motionmentioning

confidence: 99%

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Analysis and Experimental Verification for Dynamic Modeling of A Skid-Steered Wheeled Vehicle

Chuy

Collins

et al. 2010

IEEE Trans. Robot.

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“…Sliding mode controllers [ 31 , 32 ] are used for trajectory tracking. The fuzzy logic control [ 33 ] is used for a skid steering vehicle. The neural network controller is used for the steering vehicle in [ 34 ], and it is widely used in machine learning for nonlinear patterns [ 35 , 36 ].The above methods are independently used, and there are some shortcomings in terms of system control.…”

Section: Introductionmentioning

confidence: 99%

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

Gai-ning

Wen

et al. 2017

Sensors

121

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The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

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“…However, it does not have strong traction and mobility over rough and loose terrain, and hence is seldom used for outdoor terrains. Like differential steering, skid steering leads to high maneuverability Caracciolo et al (1999); Economou et al (2002); Siegwart & Nourbakhsh (2005), faster response Martinez et al (2005), and also has a simple Mandow et al (2007); Petrov et al (2000); Shamah et al (2001) and robust mechanical structure Kozlowski & Pazderski (2004); Mandow et al (2007); Yi, Zhang, Song & Jayasuriya (2007). In contrast, it also leads to strong traction and high mobilityPetrov et al (2000), which makes it suitable for all-terrain traversal.…”

mentioning

confidence: 99%

Dynamic Modeling and Power Modeling of Robotic Skid-Steered Wheeled Vehicles

Yu¹,

Collins²,

Chuy³

2011

Mobile Robots - Current Trends

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IntroductionDynamic models and power models of autonomous ground vehicles are needed to enable realistic motion planning Howard & Kelly (2007); Yu et al. (2010) in unstructured, outdoor environments that have substantial changes in elevation, consist of a variety of terrain surfaces, and/or require frequent accelerations and decelerations. At least 4 different motion planning tasks can be accomplished using appropriate dynamic and power models:1. Time optimal motion planning. Energy efficient motion planning.3. Reduction in the frequency of replanning. 4.Planning in the presence of a fault, such as flat tire or faulty motor.For the purpose of motion planning this chapter focuses on developing dynamic and power models of a skid-steered wheeled vehicle to help the above motion planning tasks. The dynamic models are the foundation to derive the power models of skid-steered wheeled vehicles. The target research platform is a skid-steered vehicle. A skid-steered vehicle can be either tracked or wheeled . Fig. 1 shows examples of a skid-steered wheeled vehicle and a skid-steered tracked vehicle. This chapter is organized into five sections. Section 1 is the introduction. Section 2 presents the kinematic models of a skid-steered wheeled vehicle, which is the preliminary knowledge to the proposed dynamic model and power model. Section 3 develops analytical dynamic models of a skid-steered wheeled vehicle for general 2D motion. The developed models are characterized by the coefficient of rolling resistance, the coefficient of friction, and the shear deformation modulus, which have terrain-dependent values. Section 4 develops analytical power models of a skid-steered vehicle and its inner and outer motors in general 2D curvilinear motion. The developed power model builds upon a previously developed dynamic model in Section 3. Section 5 experimentally verifies the proposed dynamic models and power models of a robotic skid-steered wheeled vehicle. Ackerman steering, differential steering, and skid steering are the most widely used steering mechanisms for wheeled and tracked vehicles. Ackerman steering has the advantages of good lateral stability when turning at high speeds, good controllability Siegwart & Nourbakhsh (2005) and lower power consumption Shamah et al. (2001), but has the disadvantages of low maneuverability and need of an explicit mechanical steering subsystem Mandow et al.

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Fuzzy logic approaches for wheeled skid-steer vehicles

Cited by 5 publications

References 5 publications

Analysis and Experimental Verification for Dynamic Modeling of A Skid-Steered Wheeled Vehicle

Analysis and Experimental Verification for Dynamic Modeling of A Skid-Steered Wheeled Vehicle

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

Dynamic Modeling and Power Modeling of Robotic Skid-Steered Wheeled Vehicles

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