Combined Positioning Method of Port AGV based on Beidou Difference and Inertial Navigation Technology

Luo, Kai; Zhong, Meisu; Yang, Yongsheng

doi:10.1109/issi47111.2019.9043643

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…They discussed the different positioning technologies and their advantages and disadvantages. Kai Luo et al [19] proposed a combined positioning method using the BeiDou satellite and port AGV navigation. They used a Kalman filter and a weighted average algorithm to process the positioning data, taking into account the actual port environment, and achieved good results.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved DQN Algorithm for Automated Guided Vehicle Pathfinding Problem in Port Environment

Leibing,

Wei,

et al. 2023

Preprint

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This paper introduces the D* Hindsight Deep Q-learning (DH-DQN) algorithm, which combines the D* algorithm and is applied to the port AGV path planning problem. To address the drawbacks of long decision times and large storage space of the D* algorithm, this paper proposes an improved DQN algorithm for a single AGV path planning problem. The Hindsight experience replay algorithm is used to alleviate the problem of slow or non-convergence due to sparse reward space. By selecting the actions planned by the D* algorithm with a certain probability, the DH-DQN algorithm's convergence speed and stability are improved, and its decision time is shorter than the classical D* algorithm. The DH-DQN algorithm is used to control an AGV to perform different tasks in different layouts. Experimental results show that the DH-DQN algorithm not only avoids non-convergence in four different layouts but also converges faster than the classical DQN algorithm. Furthermore, the DH-DQN and DQN algorithms' decision-making process's time comparison under the four layouts reveals that DH-DQN saves 24.91\% of the time in the large-scale environment and 28.08\% in the small-scale environment. Therefore, the DH-DQN algorithm performs well in solving the port AGV path planning problem.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved DQN Algorithm for Automated Guided Vehicle Pathfinding Problem in Port Environment

Leibing,

Wei,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to improve the availability of satellite positioning in urban areas, this method integrates GPS/BeiDou receivers, OBD-II (Onboard Diagnostic II) equipment and MEMS IMU (Micro Electro Mechanical System Inertial Measurement Unit). Luo [12] studied the combined positioning method of Beidou differential and AGV port inertial navigation. Combined with the actual environment of the port, the Kalman filter and weighted average algorithm are used to fuse the positioning data to help the AGV's real-time high-precision positioning to achieve a good fusion effect.…”

Section: Related Researchmentioning

confidence: 99%

Research on Vehicle-Road Co-Location Method Oriented to Network Slicing Service and Traffic Video

Sun

2021

Sustainability

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The development of 5G network slicing technology, combined with the application scenarios of vehicle–road collaborative positioning, provides end-to-end, large-bandwidth, low-latency, and highly reliable flexible customized services for Internet of Vehicle (IoV) services in different business scenarios. Starting from the needs of the network in the business scenario oriented to co-location, we researched the application of 5G network slicing technology in the vehicle–road cooperative localization system. We considered scheduling 5G slice resources. Creating slices to ensure the safety of the system, provided an optimized solution for the application of the vehicle–road coordinated positioning system. On this basis, this paper proposes a vehicle–road coordinated combined positioning method based on Beidou. On the basis of Beidou positioning and track estimation, using the advantages of the volumetric Kalman model, a combined positioning algorithm based on CKF was established. In order to further improve the positioning accuracy, vehicle characteristics could be extracted based on the traffic monitoring video stream to optimize the service-oriented positioning system. Considering that the vehicles in the urban traffic system can theoretically only travel on the road, the plan can be further optimized based on the road network information. It was preliminarily verified by simulation that this research idea has improved the relative single positioning method.

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Collaborative Localization Strategy Based on Node Selection and Power Allocation in Resource-Constrained Environments

Chen,

Wang,

Kong

et al. 2024

Mobile Netw Appl

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Combined Positioning Method of Port AGV based on Beidou Difference and Inertial Navigation Technology

Cited by 4 publications

References 8 publications

An Improved DQN Algorithm for Automated Guided Vehicle Pathfinding Problem in Port Environment

An Improved DQN Algorithm for Automated Guided Vehicle Pathfinding Problem in Port Environment

Research on Vehicle-Road Co-Location Method Oriented to Network Slicing Service and Traffic Video

Collaborative Localization Strategy Based on Node Selection and Power Allocation in Resource-Constrained Environments

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