Collaborative swarm robotics for sustainable environment monitoring and exploration: Emerging trends and research progress

Abstract

This study explores the application of swarm robotics and swarm and evolutionary computing techniques in environmental management and sustainability, a highly specific and increasingly demanding niche research area. Through a bibliometric analysis of two collections of peer-reviewed papers, key trends and emerging research areas are identified. The first collection, comprising approximately 450 papers, focuses on specific applications of swarm robotics systems in environmental use cases, including swarms of UAVs, AUVs, and USVs, particularly in tasks such as ecological monitoring, agricultural management, and disaster response. This analysis highlights essential keyword clusters, with ``ecological restoration'' emerging as a significant topic, and ``agricultural robots'' and ``remote sensing'' as active frontiers. Building on this analysis, eight directions are proposed to address environmental challenges across five categories. The second collection, consisting of around 198 papers, examines the different swarm and evolutionary computing algorithms employed in this niche area, identifying ten significant research clusters. Notably, the ``secure incentive mechanism'' is a trending area, emphasizing the development of reliable and secure cooperative multi-robot systems. Recent methods in this cluster utilize deep reinforcement learning and heuristic algorithms to enhance cooperation efficiency. Five potential directions categorized into two main groups are explored to address security and reliability challenges within swarm robot systems in environmental tasks. The findings underscore the critical role of swarm robotics in environment-focused tasks such as ecosystem recovery and the importance of secure cooperation mechanisms, paving the way for advancements in agriculture, resource management, intelligent infrastructure, and urban systems.