Nonlinear Model Predictive Tracking Control With C/GMRES Method for Heavy-Duty AGVs

Liu, Xingjie; Wang, Guolei; Chen, Ken

doi:10.1109/tvt.2021.3123176

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…Te MPC controller adopts a quadratic programming method to obtain the optimal linear and angular velocities as the output to control the AGV. Te work by Liu et al [22] proposes a real time nonlinear MPC control method that considers both tracking accuracy and lateral load transfer limitation for heavy duty AGVs. In this case, the AGV with a four-wheel-steering four-wheel-driving structure is simplifed into the wellknown bicycle mode.…”

Section: Related Workmentioning

confidence: 99%

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

Sánchez-Rodríguez,

Bayona,

Sierra-García

et al. 2024

Complexity

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Automated guided vehicles (AGVs), so necessary in industrial environments, require precise control of trajectory tracking to make accurate stops at logistics stations, such as loading stations, or to pick up or drop off trolleys, pallets, or racks. This paper proposes a hybrid control architecture for trajectory tracking of a hybrid tricycle-differential AGV. The control strategy combines conventional proportional integral derivative (PID) control with advanced nonlinear Lyapunov control (LPC). The LPC is used for trajectory tracking while the PID is used for speed control of the robot. The stability of the controller is demonstrated for any differentiable trajectory. When a PID optimized with genetic algorithms is compared with the proposed controller for several trajectories, the LPC outperforms it in all cases.

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Section: Related Workmentioning

confidence: 99%

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

Sánchez-Rodríguez,

Bayona,

Sierra-García

et al. 2024

Complexity

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“…AGVs have advantages such as their high work efficiency, low cost, and high safety. The research on AGVs includes navigation algorithms [9,10], path planning algorithms [11,12], path tracking algorithms [13,14], and vehicle scheduling algorithms [15,16]. Of these, path planning determines the route by which the AGV will move, and plays a fundamental role in determining the operating costs.…”

Section: Introductionmentioning

confidence: 99%

A Novel AGV Path Planning Approach for Narrow Channels Based on the Bi-RRT Algorithm with a Failure Rate Threshold

Wu,

Zhang,

Chi

et al. 2023

Sensors

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The efficiency of the rapidly exploring random tree (RRT) falls short when efficiently guiding targets through constricted-passage environments, presenting issues such as sluggish convergence speed and elevated path costs. To overcome these algorithmic limitations, we propose a narrow-channel path-finding algorithm (named NCB-RRT) based on Bi-RRT with the addition of our proposed research failure rate threshold (RFRT) concept. Firstly, a three-stage search strategy is employed to generate sampling points guided by real-time sampling failure rates. By means of the balance strategy, two randomly growing trees are established to perform searching, which improves the success rate of the algorithm in narrow channel environments, accelerating the convergence speed and reducing the number of iterations required. Secondly, the parent node re-selection and path pruning strategy are integrated. This shortens the path length and greatly reduces the number of redundant nodes and inflection points. Finally, the path is optimized by utilizing segmented quadratic Bezier curves to achieve a smooth trajectory. This research shows that the NCB-RRT algorithm is better able to adapt to the complex narrow channel environment, and the performance is also greatly improved in terms of the path length and the number of inflection points. Compared with the RRT, RRT* and Bi-RRT algorithms, the success rate is increased by 2400%, 1900% and 11.11%, respectively.

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“…Compared to the previous generation of Automated Guided Vehicles (AGVs) which were limited to fixed routes, SGVs can navigate dynamically and travel quickly. They are used mostly to perform repetitive heavy material delivery tasks [2].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position

et al. 2022

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The local path planning, as one of the navigation stages, plays a significant role in the energy consumption of Self-Guided Vehicles (SGV). Since SGV must operate for several hours on a single battery charge to transport loads, its energy consumption is a critical issue. Therefore, this article puts forward an approach for boosting the energy efficiency of the local path planning stage using load position. Unlike other similar works which solely use robots' kinematic and kinetic constraints to develop energy-efficient local path planners, this article considers the effect of load position on SGV's dynamic. In this regard, first, the kinetic model of the differential drive SGV is developed to consider the change of SGV's Center of Mass (CoM) affected by load properties. Second, machine learning methods are used to create two learning models for online estimation of the position of CoM (PoCoM) and prediction of required energy of sample trajectories. Hence, the generated SGV's kinetic model is used to train the learning models. Finally, estimated parameters are employed to add a new constraint to extend the cost function of the local path planner. The outcomes of the study show that the proposed planner generates smoother and shorter paths to pass obstacles and corridors than a general one. Thus, SGV's energy consumption decreases by considering the load effect.INDEX TERMS Energy efficiency, local path planning, dynamic, machine learning, self-guided vehicle.

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Nonlinear Model Predictive Tracking Control With C/GMRES Method for Heavy-Duty AGVs

Cited by 9 publications

References 38 publications

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

A Novel AGV Path Planning Approach for Narrow Channels Based on the Bi-RRT Algorithm with a Failure Rate Threshold

Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position

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