Experimental kinematics for wheeled skid-steer mobile robots

Mandow, Anthony; Martínez, Jorge L.; Morales, José; Blanco, Jose-Luis; García-Cerezo, Alfonso J.; González, J.

doi:10.1109/iros.2007.4399139

Cited by 152 publications

(129 citation statements)

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“…Based on the SSMR models proposed in [7], [8] and by using the Euler integration method one can obtain the discrete-time system model for (2), i.e. the 1 st EKF equation, which can be written as (7). 1 st EKF Equation: …”

Section: A Prediction Stagementioning

confidence: 99%

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Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem

Contreras¹,

Cruz²,

Motta³

et al. 2015

IJMLC

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Abstract-This paper describes a Hardware/Software Co-design approach for the Extended Kalman Filter (EKF) applied to the localization problem in mobile robotics. The EKF algorithm has been implemented and run on an Altera Cyclone IV FPGA with a Nios II embedded processor jointly with specific hardware modules, being adapted and applied to the mobile platform Pioneer 3AT (P3AT). In order to achieve this, we developed both the model of the mobile robot and its measurement systems previously to obtain the respective EKF equations. In the prediction step of the EKF algorithm, a system model based on concepts of dead-reckoning has been used and its implementation was achieved in software, using the Nios II processor. Conversely, in the estimation step of the EKF algorithm the respective equations have been implemented directly in hardware, producing an overall balanced implementation.Index Terms-Dead-reckoning, extended Kalman filter, FPGA, hardware/software co-design, Pioneer 3AT. I. INTRODUCTIONThere are several works describing the implementation of probabilistic algorithm in FPGAs such as described in [1]- [3]. Among them, the Kalman Filter (KF) is one of the most useful algorithms for estimating the state of a dynamic system [3]. A special case of KF is the Extended Kalman Filter, which application is oriented to nonlinear systems [4].This work presents a hardware/software co-design approach in FPGA of the Extended Kalman Filter Algorithm aiming at solving the Localization Problem in Mobile Robotics. The algorithm implementation was achieved considering the following approach: 1) a software implementation in the Nios II processor of the prediction stage (namely, 1 st and 2 nd EKF algorithm equations) and 2) a hardware implementation in Altera Cyclone IV FPGA of the estimation stage (namely 3 rd , 4 th and 5 th EKF algorithm equations). For this case, it has been considered that the mobile robot is located within an environment with a known map and it moves using data processing on line. In this case, the mobile platform used in this project was the Pioneer 3-AT [5].In this context, the contributions of this paper include: 1) the implementation of a sequential EKF algorithm in FPGA based on an approach of hardware/software co-design for the stages of prediction and estimation, using floating-point representation, and 2) the validation of the results in terms of hardware recourse consumption, performance and functionality in a real environment using the robot platform. II. THE EKF ALGORITHMThe KF is based on a recursive method to estimate the state variable (x K ) of a linear dynamic system with the process noise (w K ) and measurement noise (v K ) [6]. An extension of the Kalman Filter is the EKF algorithm (Extended Kalman Filter), which is used for nonlinear dynamical systems. The EKF tries to linearize the system around the current state. For this case, we will consider a nonlinear system represented by:where f(.) is the nonlinear function of the process system, h(.) is the nonlinear function of the measurement...

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Section: A Prediction Stagementioning

confidence: 99%

“…The parameter D is the effective spacing between the wheels. The error associated to the linear tread speeds of and can be defined such as described in (8).…”

Section: mentioning

confidence: 99%

Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem

Contreras¹,

Cruz²,

Motta³

et al. 2015

IJMLC

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show abstract

“…Alternatively, a combination between kinematic and dynamic models can be investigated, [4,10,24]. Another approach is based on kinematic approximation of nonitegrable velocity constraints, [3,9,18].…”

Section: Introductionmentioning

confidence: 99%

Waypoint Following for Differentially Driven Wheeled Robots with Limited Velocity Perturbations

Pazderski

2016

J Intell Robot Syst

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In this paper a unified motion control strategy dedicated for the waypoint following task realized by a differentially driven robot is presented. It is assumed that the vehicle moves with limited velocities and accelerations in order to reduce excessive slip and skid effects. In order to include operational constraints, a motion planner is combined with a universal stabilizer taking advantage of transverse functions. To improve tracking precision translated transverse functions are deployed and a new adaptive technique for the controller tuning is proposed. During the motion planning stage an auxiliary trajectory connecting points in the configuration space and satisfying assumed phase constraints is generated. The resulting motion execution system has been implemented on a laboratory-scale skid-steering mobile robot, which served as platform for experimental validation of presented algorithms.

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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.…”

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confidence: 99%

“…Many of the difficulties associated with modeling and operating both classes of skid-steered vehicles arise from the complex wheel (or track) and terrain interaction Mandow et al (2007); Yi, Song, Zhang & Goodwin (2007). For Ackerman-steered or differential-steered vehicles, the wheel motions may often be accurately modeled by pure rolling, while for skid-steered vehicles in general curvilinear motion, the wheels (or tracks) roll and slide at the same time Mandow et al (2007); O. Chuy et al (2009) ;Yi, Song, Zhang & Goodwin (2007) ;Yi, Zhang, Song & Jayasuriya (2007).…”

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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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Experimental kinematics for wheeled skid-steer mobile robots

Cited by 152 publications

References 10 publications

Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem

Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem

Waypoint Following for Differentially Driven Wheeled Robots with Limited Velocity Perturbations

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

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