Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework

Chen, Xinqiang; Liu, Shuhao; Zhao, Jiansen; Wu, Huafeng; Xian, Jiangfeng; Montewka, Jakub

doi:10.1016/j.ocecoaman.2024.107087

Cited by 36 publications

(10 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AGV (Automated Guided Vehicle) is an automatic guided vehicle capable of autonomous navigation without human intervention [1] , which has been widely used in logistics [2] , medicine [3] , shipping [4] , industry [5] and other scenarios.With the rapid development of AGV technology [6] , in the medical field, as an automated transport equipment, carrying AGV has received more and more attention and gradually developed in the direction of intelligence [7] .Therefore, it is a key research direction for AGVs to safely avoid dynamic and static obstacles such as pedestrians and medical devices in the medical environment [8] , and to reasonably plan a path from the starting point to the target point [9] . At present, scholars from different countries have made a series of research and improvement on the path planning algorithm and the limitations of the algorithm itself [10] .…”

Section: Introductionmentioning

confidence: 99%

Obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Hu,

Niu,

Gao

et al. 2024

Preprint

View full text Add to dashboard Cite

Aiming at the problem that Automated guided vehicle (AGV) faces dynamic and static obstacles in the process of transporting patients in medical environment and needs to avoid in real time, inspired by the behavior of antelopes during migration, this paper proposes a bionic obstacle avoidance strategy based on adaptive behavior of antelopes. The traditional artificial potential field and dynamic window algorithm are improved by using the bionic characteristics of antelope migration.By adding new potential field force points and improving the window size, the success rate and prediction range of AGV navigation are improved.Simulation experiments were carried out through the numerical simulation platform, and the verification results showed that:The bionic obstacle avoidance strategy proposed in this paper can avoid dynamic and static obstacles at the same time. In the example, the success rate of path planning is increased by 34%, the running time is reduced by 33%, and the average path length is reduced by 1%. The proposed method can realize the integration of “dynamic and static” avoidance in the process of transporting patients, and effectively save the time of transporting patients by AGV.It provides a theoretical basis for realizing obstacle avoidance and rapid loading of AGV in medical environment.

show abstract

Section: Introductionmentioning

confidence: 99%

Obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Hu,

Niu,

Gao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Research on dynamic and static obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Hu,

Niu,

Gao

et al. 2024

Preprint

View full text Add to dashboard Cite

Aiming at the problem that Automated guided vehicle (AGV) faces dynamic and static obstacles in the process of transporting patients in medical environment and needs to avoid in real time, inspired by the behavior of antelopes during migration, this paper proposes a bionic obstacle avoidance strategy based on adaptive behavior of antelopes. The artificial potential field and dynamic window algorithm are improved by using the decircling strategy and conical visual distance during antelope migration. The limitations of traditional artificial potential field algorithm are improved by adding new potential field force points into the global planning, and the real-time obstacle avoidance ability of medical AGV is enhanced. The evaluation index of the algorithm is improved in local path planning, and the prediction range of AGV navigation is enlarged. Simulation experiments were carried out through the numerical simulation platform, and the verification results showed that:The bionic obstacle avoidance strategy proposed in this paper can avoid dynamic and static obstacles at the same time. In the example, the success rate of path planning is increased by 34%, the running time is reduced by 33%, and the average path length is reduced by 1%. The proposed method can realize the integration of "dynamic and static" avoidance in the process of transporting patients, and effectively save the time of transporting patients by AGV.It provides a theoretical basis for realizing obstacle avoidance and rapid loading of AGV in medical environment.

show abstract

“…In recent years, deep learning models [22] and geographic information systems (GISs) have also been used to predict the geographical coordinates of past merchant ships and alternative islands [23][24][25]. By employing the K-means clustering algorithm, they determined the positions of dynamic work points for establishing SAR bases at sea.…”

Section: Introductionmentioning

confidence: 99%

Research on the Deployment of Professional Rescue Ships for Maritime Traffic Safety under Limited Conditions

Shao,

Wu,

et al. 2024

JMSE

View full text Add to dashboard Cite

This paper focuses on optimizing the deployment plan for standby points of professional rescue vessels based on the data of maritime incidents in the Beihai area of China. The primary objective is to achieve multi-level and multiple coverage of the jurisdictional waters of the Beihai Rescue Bureau. Models including the coverage quality of the jurisdictional waters, the coverage quality in high-risk areas, the maximum coverage of jurisdictional areas, and the maximum coverage of high-risk areas are constructed and solved using 0–1 integer programming. The optimal plan for eight standby points and their corresponding deployment plans for rescue vessels are obtained. A comparison with the current site selection plan of the Beihai Rescue Bureau validates the superiority of the proposed deployment plan for rescue vessel standby points in this paper.

show abstract

Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework

Cited by 36 publications

References 49 publications

Obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Research on dynamic and static obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Research on the Deployment of Professional Rescue Ships for Maritime Traffic Safety under Limited Conditions

Contact Info

Product

Resources

About