Pallet detection and docking strategy for autonomous pallet truck AGV operation

Tsiogas, Efthimios; Kleitsiotis, Ioannis; Kostavelis, Ioannis; Kargakos, Ανδρέας; Giakoumis, Dimitrios; Bosch-Jorge, Marc; Ros, Raquel Julia; Tarazón, Rafa López; Likothanassis, Spyridon; Tzovaras, Dimitrios

doi:10.1109/iros51168.2021.9636270

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…This approach allows the robot to dock without a complete stop by aligning the direction and distance between the robot and the docking station. Tsiogas [19] introduced a vision-based approach that enables the safe and autonomous operation of pallet moving vehicles, and Ding [20] proposed a combination of the double-wheel chassis model (DCM) and PID control strategy to enhance the precision and stability during docking. Moreover, visual strategies are applied for accurate docking alignment.…”

Section: Related Workmentioning

confidence: 99%

An Accurate and Rapid Docking Algorithm for Four-Way Shuttle in High-Density 3D Warehousing Environment

Liu,

Song

et al. 2024

Machines

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High-density 3D warehousing is the future cornerstone of modern logistics storage. It aims to efficiently manage and store various types of goods by utilizing 3D space and automation technology. The accurate and fast docking algorithm of the four-way shuttles, as a core method of carrying the goods in high-density 3D warehousing, has become an increasingly important technical challenge. Inaccurate docking will result in a failure to change direction, while long docking time will affect the operation efficiency of the four-way shuttle. To overcome these obstacles, this paper presents an accurate and rapid docking algorithm for four-way shuttles. Firstly, the deceleration and jerk of a four-way shuttle in the braking stage are initialized according to the motion parameter set calculated by the docking motion model. Then, the four-way shuttle starts to move until the stop signal has been detected. If the system fails to detect the stop signal, the distance compensation is needed to ensure that the four-way shuttle can arrive at the stop point. Finally, the four-way shuttle stops immediately by way of amplifying the deceleration when reaching the stop point. The result shows that the accuracy of rapid docking is superior to that of direct docking. Compared with crawling docking, rapid docking reduces the braking time by 3.99s and stops at a speed lower than the crawling speed. The rapid docking algorithm not only enhances the throughput of high-density 3D warehousing but also improves the service life of four-way shuttles.

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Section: Related Workmentioning

confidence: 99%

An Accurate and Rapid Docking Algorithm for Four-Way Shuttle in High-Density 3D Warehousing Environment

Liu,

Song

et al. 2024

Machines

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“…Such systems usually require artificially added features on the pallet, like fiducial markers (Tsiogas et al , 2021), or using the pallet’s features, such as colour, shape and other structural information. However, associating with the pallet is highly undesirable in the daily warehouse operation.…”

Section: Related Workmentioning

confidence: 99%

“…RGB imagery or point cloud data, and are limited by either localization precision or recognition accuracy. For example, researchers used a two-dimensional (2D) laser rangefinder and applied feature detection to locate the pallets (Wang et al , 2021; Tsiogas et al , 2021). However, this method suffered from pallets varying in size and shape, as it could not capture enough features from the 2D depth information (Mohamed et al , 2020; Molter and Fottner, 2018).…”

Section: Introductionmentioning

confidence: 99%

A systematic strategy of pallet identification and picking based on deep learning techniques

Li¹,

Ding²,

Li³

et al. 2023

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Purpose This paper presents a comprehensive pallet-picking approach for forklift robots, comprising a pallet identification and localization algorithm (PILA) to detect and locate the pallet and a vehicle alignment algorithm (VAA) to align the vehicle fork arms with the targeted pallet. Design/methodology/approach Opposing vision-based methods or point cloud data strategies, we utilize a low-cost RGB-D camera, and thus PILA exploits both RGB and depth data to quickly and precisely recognize and localize the pallet. The developed method guarantees a high identification rate from RGB images and more precise 3D localization information than a depth camera. Additionally, a deep neural network (DNN) method is applied to detect and locate the pallet in the RGB images. Specifically, the point cloud data is correlated with the labeled region of interest (RoI) in the RGB images, and the pallet's front-face plane is extracted from the point cloud. Furthermore, PILA introduces a universal geometrical rule to identify the pallet's center as a “T-shape” without depending on specific pallet types. Finally, VAA is proposed to implement the vehicle approaching and pallet picking operations as a “proof-of-concept” to test PILA’s performance. Findings Experimentally, the orientation angle and centric location of the two kinds of pallets are investigated without any artificial marking. The results show that the pallet could be located with a three-dimensional localization accuracy of 1 cm and an angle resolution of 0.4 degrees at a distance of 3 m with the vehicle control algorithm. Research limitations/implications PILA’s performance is limited by the current depth camera’s range (< = 3 m), and this is expected to be improved by using a better depth measurement device in the future. Originality/value The results demonstrate that the pallets can be located with an accuracy of 1cm along the x, y, and z directions and affording an angular resolution of 0.4 degrees at a distance of 3m in 700ms.

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“…Docking occurs at the final stage of navigation, using various sensors to update the target location and approach the target at a relatively low speed. The literature reports several docking techniques, which can be classified according to the type of sensor used, such as camera-based [10][11][12][13], laser-based [14][15][16], and optics-based [17] sensors, among others [18][19][20].…”

Section: Related Workmentioning

confidence: 99%

FiMa-Reader: A Cost-Effective Fiducial Marker Reader System for Autonomous Mobile Robot Docking in Manufacturing Environments

Bian,

Chen,

Ran

et al. 2023

Applied Sciences

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Accurately docking mobile robots to various workstations on the factory floor is a common and essential task. The existing docking methods face three major challenges: intricate deployment procedures, susceptibility to ambient lighting, and incapacity to recognize product information during the docking process. This paper devises a novel approach that combines the features of ArUco and Data Matrix to form a composite marker termed “DataMatrix-ArUco-Hybrid” (DAH). The DAH pattern serves as a fiducial marker capable of concurrently providing both target pose information and product information. Detection of the DAH pattern is conducted by a cost-effective fiducial marker reader system, called “FiMa-Reader”, which comprises an embedded processing unit and an infrared camera equipped with a 940 nm fill-light to overcome lighting issues. The FiMa-Reader system effectively detects the DAH pattern under both well-lit and dimly lit conditions. Additionally, the implementation of the FiMa-Reader system leads to significant improvements in positioning accuracy, including an 86.42% improvement on the x-axis, a 44.7% improvement on the y-axis, and an 84.21% improvement in angular orientation when compared to traditional navigation methods. The utilization of FiMa-Reader presents an economically viable system capable of guiding mobile robots’ positioning with high precision in various indoor lighting conditions.

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Pallet detection and docking strategy for autonomous pallet truck AGV operation

Cited by 10 publications

References 22 publications

An Accurate and Rapid Docking Algorithm for Four-Way Shuttle in High-Density 3D Warehousing Environment

An Accurate and Rapid Docking Algorithm for Four-Way Shuttle in High-Density 3D Warehousing Environment

A systematic strategy of pallet identification and picking based on deep learning techniques

FiMa-Reader: A Cost-Effective Fiducial Marker Reader System for Autonomous Mobile Robot Docking in Manufacturing Environments

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