@inproceedings{31ba46baeb95419aaf5cec13888cc47f,
title = "Level of Priority based Leader-Following Behavior using Reciprocal Velocity Obstacles in Multi-Agents Navigation",
abstract = "Leader-following behavior is a behavior in which a number of follower agents are required to go after the leader. The leader agent is tasked to lead the group to reach their goal. This leader holds a significant role for a group of leader-following agents to reach their shared destination since the leader has full information of the whole area of navigation. Due to its importance, leader movement supplicate hindrance-free navigation. Considering the follower agents have minimal information regarding path information, they tend to stick with their leader. These followers' actions may affect leader movement thus creating a disturbance that drives the group into navigation failure such as deadlock. This research utilized leader-following behavior with levels of priority between the leader and follower agents in the avoidance process. We employ Reciprocal Velocity Obstacles(RVO) to handle the avoidance process. Levels of priority enforced difference weight in avoidance effort, thus creating an absolute necessity for the followers to fully accomplish avoidance on their own against their respective leader. We conduct a series of experiments to reveal the problem that caused by followers which hinders the leader's navigation. We also exemplify that our method can successfully provide necessary action to satisfy leader navigation. From the results of the experiments we conduct, the leader's movement is safe from deadlocks caused by followers blocking the leader agent movement.",
keywords = "Crowd Simulation, Leader-Following, Multi-Agent Navigation, RVO(Reciprocal Velocity Obstacles)",
author = "Moch Fachri and Susi Juniastuti and Nugroho, {Supeno Mardi Susiki} and Mochamad Hariadi",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2021 International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2021 ; Conference date: 29-01-2022",
year = "2022",
doi = "10.1109/ISMODE53584.2022.9743017",
language = "English",
series = "2021 International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "45--50",
booktitle = "2021 International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2021",
address = "United States",
}