Formation Control of Automated Guided Vehicles in the Presence of Packet Loss

Sedghi, Leila; John, Jobish; Noor-A-Rahim, Md.; Pesch, Dirk

doi:10.3390/s22093552

Cited by 4 publications

(4 citation statements)

References 54 publications

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“…FDE denotes the Euclidean distance difference on end position between the predicted trajectory and the true trajectory, which is given by (9).…”

Section: Evaluaiton Metricsmentioning

confidence: 99%

“…However, Vehicle-to-vehicle (V2V) is also a source for trajectory prediction input. However, in the case that the V2V communication technique is unavailable, autonomous vehicles cannot receive accurate information from surrounding vehicles, and the autonomous vehicles have to deduce the trajectory of other vehicles through various onboard sensors [8][9][10]. Currently, a lot of research has captured PTVs using cameras, LIDAR, and other sensors in real road driving [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Trajectory Prediction of Preceding Target Vehicles in Urban Scenario Using Multi-Sensor Fusion

Zou

Hou

et al. 2022

Sensors

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Preceding vehicles have a significant impact on the safety of the vehicle, whether or not it has the same driving direction as an ego-vehicle. Reliable trajectory prediction of preceding vehicles is crucial for making safer planning. In this paper, we propose a framework for trajectory prediction of preceding target vehicles in an urban scenario using multi-sensor fusion. First, the preceding target vehicles historical trajectory is acquired using LIDAR, camera, and combined inertial navigation system fusion in the dynamic scene. Next, the Savitzky–Golay filter is taken to smooth the vehicle trajectory. Then, two transformer-based networks are built to predict preceding target vehicles’ future trajectory, which are the traditional transformer and the cluster-based transformer. In a traditional transformer, preceding target vehicles trajectories are predicted using velocities in the X-axis and Y-axis. In the cluster-based transformer, the k-means algorithm and transformer are combined to predict trajectory in a high-dimensional space based on classification. Driving data from the real-world environment in Wuhan, China, are collected to train and validate the proposed preceding target vehicles trajectory prediction algorithm in the experiments. The result of the performance analysis confirms that the proposed two transformers methods can effectively predict the trajectory using multi-sensor fusion and cluster-based transformer method can achieve better performance than the traditional transformer.

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“…FDE denotes the Euclidean distance difference on end position between the predicted trajectory and the true trajectory, which is given by (9).…”

Section: Evaluaiton Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Framework for Trajectory Prediction of Preceding Target Vehicles in Urban Scenario Using Multi-Sensor Fusion

Zou

Hou

et al. 2022

Sensors

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“…Important research works concern the evaluation of architectural patterns for the remote control of AGVs [29], live visualizations for remote AGV operation [30] and formation control of AGVs in the presence of package losses [31]. As reflected in the research of Sedghi et al [31], the influence of faults can be especially high, when AGVs need to interact in order to achieve a common task. A very important area for scientific investigation covers the formation control of AGVs.…”

Section: State Of the Art In Fault-tolerant Controlmentioning

confidence: 99%

“…Current research investigates communications-based formation control [32] and consensus based control for dynamic environments [33]. A promising future research field will be fault-tolerant formation control-research concerning formation control in the presence of package losses points in this direction [31]. In formation control many uncertain conditions are present and formation control problem with external interference should be studied in the future [34].…”

Section: State Of the Art In Fault-tolerant Controlmentioning

confidence: 99%

Algorithms and Methods for the Fault-Tolerant Design of an Automated Guided Vehicle

Stetter

2022

Sensors

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Researchers around the globe have contributed for many years to the research field of fault-tolerant control; the importance of this field is ever increasing as a consequence of the rising complexity of technical systems, the enlarging importance of electronics and software as well as the widening share of interconnected and cloud solutions. This field was supplemented in recent years by fault-tolerant design. Two main goals of fault-tolerant design can be distinguished. The first main goal is the improvement of the controllability and diagnosability of technical systems through intelligent design. The second goal is the enhancement of the fault-tolerance of technical systems by means of inherently fault-tolerant design characteristics. Inherently fault-tolerant design characteristics are, for instance, redundancy or over-actuation. This paper describes algorithms, methods and tools of fault-tolerant design and an application of the concept to an automated guided vehicle (AGV). This application took place on different levels ranging from conscious requirements management to redundant elements, which were consciously chosen, on the most concrete level of a technical system, i.e., the product geometry. The main scientific contribution of the paper is a methodical framework for fault-tolerant design, as well as certain algorithms and methods within this framework. The underlying motivation is to support engineers in design and control trough product development process transparency and appropriate algorithms and methods.

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Impacts of communication delay on vehicle platoon string stability and its compensation strategy: A review

Zhang¹,

Xu²,

Wang³

et al. 2023

Journal of Traffic and Transportation Engineering (English Edit

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Formation Control of Automated Guided Vehicles in the Presence of Packet Loss

Cited by 4 publications

References 54 publications

A Framework for Trajectory Prediction of Preceding Target Vehicles in Urban Scenario Using Multi-Sensor Fusion

A Framework for Trajectory Prediction of Preceding Target Vehicles in Urban Scenario Using Multi-Sensor Fusion

Algorithms and Methods for the Fault-Tolerant Design of an Automated Guided Vehicle

Impacts of communication delay on vehicle platoon string stability and its compensation strategy: A review

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