Non-linear model predictive control for constrained robot navigation in row crops

Utstumo, Trygve; Berge, Therese W.; Gravdahl, Jan Tommy

doi:10.1109/icit.2015.7125124

Cited by 14 publications

(9 citation statements)

References 13 publications

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“…For the stability analysis of the kinematic controller, it is supposed a perfect velocity tracking, which allows equating (19) and (20) under the assumption of u u d and ω ω d , which means that the dynamic effects are, at this moment, ignored. Then, the closed-loop equation is obtained in terms of the velocity errors, which is…”

Section: Kinematic Controllermentioning

confidence: 99%

“…Assuming u u d and ω ω d , Eq. (20) guarantees that ω ¼ 0 whenx ¼ 0 andỹ ¼ 0, therefore ψ t ðÞ!ψ constant .…”

Section: Applications Of Mobile Robotsmentioning

confidence: 99%

“…In our example, the trajectory tracking controller given by Eq. (20) was used. This is the system that we have implemented and for which we present some experimental results in Section 5.…”

Section: Dynamic Compensation Controllermentioning

confidence: 99%

“…Because of that, some researchers have proposed dynamic controllers that generate linear and angular velocities as commands [15,16]. In some works, the dynamic model is divided in to two parts, allowing the design of independent controllers for the robot kinematics and dynamics [17][18][19][20]. Finally, to reduce performance degradation in applications in which the robot dynamic parameters may vary (such as load transportation) or when the knowledge of the dynamic parameters is imprecise, adaptive controllers can also be considered [7,21].…”

Section: Introductionmentioning

confidence: 99%

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Motion Control and Velocity-Based Dynamic Compensation for Mobile Robots

Martins

Brandão²

2019

Applications of Mobile Robots

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The design of motion controllers for wheeled mobile robots is often based only on the robot's kinematics. However, to reduce tracking error it is important to also consider the robot dynamics, especially when high-speed movements and/or heavy load transportation are required. Commercial mobile robots usually have internal controllers that accept velocity commands, but the control signals generated by most dynamic controllers in the literature are torques or voltages. In this chapter, we present a velocity-based dynamic model for differential-drive mobile robots that also includes the dynamics of the robot actuators. Such model can be used to design controllers that generate velocity commands, while compensating for the robot dynamics. We present an explanation on how to obtain the parameters of the dynamic model and show that motion controllers designed for the robot's kinematics can be easily integrated with the velocity-based dynamic compensation controller. We conclude the chapter with experimental results of a trajectory tracking controller that show a reduction of up to 50% in tracking error index IAE due to the application of the dynamic compensation controller.

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Section: Kinematic Controllermentioning

confidence: 99%

“…Assuming u u d and ω ω d , Eq. (20) guarantees that ω ¼ 0 whenx ¼ 0 andỹ ¼ 0, therefore ψ t ðÞ!ψ constant .…”

Section: Applications Of Mobile Robotsmentioning

confidence: 99%

“…In our example, the trajectory tracking controller given by Eq. (20) was used. This is the system that we have implemented and for which we present some experimental results in Section 5.…”

Section: Dynamic Compensation Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Motion Control and Velocity-Based Dynamic Compensation for Mobile Robots

Martins

Brandão²

2019

Applications of Mobile Robots

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“…The robot uses a downward facing camera to identify weeds and a nozzle array applies the herbicide as the robot passes over them, (Urdal et al, 2014;Utstumo and Gravdahl, 2013;Utstumo et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Unscented Multi-Point Smoother for Fusion of Delayed Displacement Measurements: Application to Agricultural Robots

Arbo¹,

Utstumo²,

Brekke³

et al. 2017

MIC

Self Cite

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Visual Odometry (VO) is increasingly a useful tool for robotic navigation in a variety of applications, including weed removal for agricultural robotics. The methods of evaluating VO are often computationally expensive and can cause the VO measurements to be significantly delayed with respect to a compass, wheel odometry, and GPS measurements. In this paper we present a Bayesian formulation of fusing delayed displacement measurements. We implement solutions to this problem based on the unscented Kalman filter (UKF), leading to what we term an unscented multi-point smoother. The proposed methods are tested in simulations of an agricultural robot. The simulations show improvements in the localization RMS error when including the VO measurements with a variety of latencies.

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High‐precision control of tracked field robots in the presence of unknown traction coefficients

Kayacan

Young

Peschel

et al. 2018

Journal of Field Robotics

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Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off‐track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real‐time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high‐frequency (milliseconds) updates. A real‐time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in‐field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time‐varying parameters. The capabilities of the real‐time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. The mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.

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Non-linear model predictive control for constrained robot navigation in row crops

Cited by 14 publications

References 13 publications

Motion Control and Velocity-Based Dynamic Compensation for Mobile Robots

Motion Control and Velocity-Based Dynamic Compensation for Mobile Robots

Unscented Multi-Point Smoother for Fusion of Delayed Displacement Measurements: Application to Agricultural Robots

High‐precision control of tracked field robots in the presence of unknown traction coefficients

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