A Comprehensive Assessment of Decision Making, Communication and Data Extraction in Multi-Agent System

This research focuses on assessing the application of agent-based communication and self-navigation to achieve tasks such as image fusion. In this multi-agent system experiment, agents are tasked with a common goal: accessing a shared environment with no more than a 5% overlap between each other. The resulting data—whether images, video, or other types of recordings—are fused or merged to create a comprehensive view of the accessed area. Various communication patterns (topologies) are employed within the multi-agent system to minimize communication delays. Agents utilize Mesh, Star, or Ring topologies as needed, and based on the topology applied at any given instance, a lead agent assists with baseline orientation, referencing the positions and angles of other agents for efficient navigation and data collection. Through this collective effort, the system aims to create a comprehensive representation of the object or dataset by combining multiple images. This synthesis enhances the system's capacity for thorough analysis and informed decision-making. At its core, the primary objective of this endeavor is to address existing research gaps concerning communication self-navigation and image (data) fusion within multi-agent systems. The focus lies in developing a methodology that enables agents to autonomously navigate and communicate, guided by their specific interests in target objects. This autonomy empowers agents to efficiently gather data and collaborate towards identifying and analyzing objects of interest. Central to achieving this objective is the optimization of communication protocols within the multi-agent system. This optimization involves transitioning between different topology systems, each meticulously designed to handle delays caused by specific perturbations affecting the system. By adapting the communication topology to the prevailing conditions, the research aims to enhance the efficiency and reliability of data exchange among agents. Moreover, the research endeavors to strengthen coordination within the multi-agent system, particularly in scenarios where communication is challenged or contested. This is achieved through the implementation of refined data pooling strategies, wherein agents collaborate to aggregate and analyze data effectively. By optimizing data pooling methodologies, the research aims to improve the accuracy and reliability of decision-making processes within the system. An essential aspect is conducting a comprehensive literature review to identify potential enhancements in agents' communication strategies based on the importance of the data being exchanged. By leveraging insights from existing research, the development of robust communication protocols tailored to the specific needs and objectives of the multi-agent system is presented.