Graphical Voronoi study for enhancing pursuer-evader robot games strategies: improved spatial awareness and decision-making

This research examines a differential multi-agent pursuit-evasion capture game based on an improved graphical Voronoi partition method. The objective is to capture all evaders within a specified time limit. By applying the modified Voronoi graph technique, every pursuer can detect nearby evaders and move to catch them. The pursuer performs capturing by surrounding the evader within its Voronoi cell. As the pursuit intensifies, the area of the evader's cell shrinks since the agents work in a constrained environment. The general applicability of this graphical method for resolving multi-agent pursuit-evasion games is shown using Voronoi partitioning and control policies. The Voronoi diagram provides a discrete abstraction of the continuous space which enables decentralised collision-free motion planning. To assess the computational difficulty of the multi-agent capture method, the joint capture conditions are established. The pursuers coordinate based on local information to converge on the evaders. This research provides a novel graph-based technique for cooperative multi-agent capture scenarios and may have broader applications in robotics and distributed control. Overall, this work establishes the theoretical foundations and demonstrates the universality of an improved Voronoi-based approach for multi-pursuer multi-evader capture.

Authors

Institutions

Publication Details

Journal
International Journal of Modelling Identification and Control
Published
2025-01-01
DOI
https://doi.org/10.1504/ijmic.2025.147945
Primary Topic
Guidance and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Graphical Voronoi study for enhancing pursuer-evader robot games strategies: improved spatial awareness and decision-making

Imen Ben Omrane, Hassene Seddik, Khaled Khnissi, Ahmad Hably
International Journal of Modelling Identification and Control
Guidance and Control Systems
article

Graphical Voronoi study for enhancing pursuer-evader robot games strategies: improved spatial awareness and decision-making

Imen Ben Omrane, Hassene Seddik, Khaled Khnissi, Ahmad Hably
article en

Abstract

This research examines a differential multi-agent pursuit-evasion capture game based on an improved graphical Voronoi partition method. The objective is to capture all evaders within a specified time limit. By applying the modified Voronoi graph technique, every pursuer can detect nearby evaders and move to catch them. The pursuer performs capturing by surrounding the evader within its Voronoi cell. As the pursuit intensifies, the area of the evader's cell shrinks since the agents work in a constrained environment. The general applicability of this graphical method for resolving multi-agent pursuit-evasion games is shown using Voronoi partitioning and control policies. The Voronoi diagram provides a discrete abstraction of the continuous space which enables decentralised collision-free motion planning. To assess the computational difficulty of the multi-agent capture method, the joint capture conditions are established. The pursuers coordinate based on local information to converge on the evaders. This research provides a novel graph-based technique for cooperative multi-agent capture scenarios and may have broader applications in robotics and distributed control. Overall, this work establishes the theoretical foundations and demonstrates the universality of an improved Voronoi-based approach for multi-pursuer multi-evader capture.

International Journal of Modelling Identification and ControlVol. 46(1)
Institut polytechnique de Grenoble (FR), Centre National de la Recherche Scientifique (FR), National Engineering School of Tunis (TN), Université Grenoble Alpes (FR)
Peace, Justice and strong institutions
Openalex Percentile: Top 71%
Guidance and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.