https://blog.pouceheure.fr/tags/multi_agent_systems/Recent content in Multi_agent_systems on Hugo POUSSEURHugoenThu, 28 Mar 2024 00:00:00 +0000https://blog.pouceheure.fr/articles/thesis/Thu, 28 Mar 2024 00:00:00 +0000https://blog.pouceheure.fr/articles/thesis/Abstract My thesis is based on shared navigation between the autonomous system and the human. In our research, we focus on command fusion. In our approach, both entities, the human and the autonomous system, simultaneously control the vehicle, and a module acquires their commands and performs the command fusion. This approach involves studying the intentions of both the human and the autonomous system to ensure the most appropriate fusion of their choices and to evaluate the decision-making of each entity.https://blog.pouceheure.fr/articles/cooperative-architecture/Sun, 01 Jan 2023 00:00:00 +0000https://blog.pouceheure.fr/articles/cooperative-architecture/Abstract A cooperative navigation architecture for the search and recognition of targets using aerial and ground vehicles is proposed in this paper. The architecture allows managing aerial and ground vehicles to autonomously perform different tasks independently or cooperatively. For our application, two main tasks are conceived: aerial monitoring of a surface to search for targets and target ground recognition. In the target aerial detection task, the aerial drone autonomously follows a trajectory computed to cover the entire surface to be monitored, searching for targets using vision algorithms.https://blog.pouceheure.fr/projects/multi-robots/Wed, 10 Aug 2022 00:00:00 +0000https://blog.pouceheure.fr/projects/multi-robots/Context As part of a research internship project, a modular ROS architecture was designed to enable cooperative navigation between heterogeneous autonomous vehicles: drones (UAVs) and ground robots (UGVs). The contribution focused on supporting the development and implementation of this system, which aimed to be scalable, with dynamic task allocation and coordinated execution across different hardware platforms. The architecture uses a unified communication protocol, distributed ROS nodes, and a namespace layout allowing multiple robots to be managed from a central ROS master.https://blog.pouceheure.fr/projects/light-mas/Mon, 07 Sep 2020 00:00:00 +0000https://blog.pouceheure.fr/projects/light-mas/Overview py_light_mas is a lightweight Python framework for multi-agent systems (MAS). It provides simple abstractions for Agents, an Environnemnt, a Simulation loop, and an optional Network for inter-agent communication. Architecture The architecture can be illustrated in two diagrams that describe the same system: Fig. 1 - Relations between Simulation, Environnemnt, Agents, and Network. The Fig. 1 highlights how the Simulation connects the Environnemnt, Agents, and optional Network. Fig. 2 - Event sequence across a simulation tick with message passing.https://blog.pouceheure.fr/articles/jade-microgrids/Wed, 01 Jan 2020 00:00:00 +0000https://blog.pouceheure.fr/articles/jade-microgrids/Abstract This work presents a JADE-based model for optimizing microgrid energy distribution. The proposed approach utilizes a multi-agent system framework to manage and control various distributed energy resources within a microgrid. By implementing intelligent agent behaviors, the system aims to enhance energy efficiency, reliability, and scalability in smart grid applications. Simulation results demonstrate the effectiveness of the JADE-based model in handling dynamic energy demands and supply conditions, contributing to the advancement of intelligent energy management systems.https://blog.pouceheure.fr/articles/multi-agent-smart-houses/Mon, 01 Jan 2018 00:00:00 +0000https://blog.pouceheure.fr/articles/multi-agent-smart-houses/Abstract Most demand-side management programs focus on the interactions between an aggregator and its users. Moreover, renewable energy production is irregular; increasing their number implies predicting consumption and managing energy storage or discharge in real time. Therefore, organizing the consumption patterns of every device connected to the grid is essential to optimize the global consumption. Studying the smart grid through modeling and simulation provides valuable results that cannot be obtained in the real world due to time and cost constraints.