A Survey of Hybrid Energy-Aware and Decentralized Game-Theoretic Approaches in Intelligent Multi-Robot Task Allocation

Multi-Robot Task Allocation (MRTA) has proven its importance in the current and near-future era, wherein in every aspect of life, there will be robots to handle tasks effectively and efficiently. While there has been a growing interest in MRTA problems in the robotics industry, the question arises of how to make robots more decentralized and intelligent through rational decision-makers rather than ones that are centralized and filled with black boxes. This survey aims to address that question by examining recent MRTA literature and exploring topics including MRTA taxonomy, centralized and decentralized controls, static and dynamic allocation strategies, heterogeneity and coalition formations, energy harvesting, game theory, and hybrid MRTA strategies for intelligent task management and decision-making. The main motivation of this survey is to establish a unique understanding of intelligent MRTA by comparing the existing literature through diverse MRTA perspectives. In this regard, we perform a comprehensive analysis of the recent MRTA papers and provide relative discussions in each section. With this survey, we try to pose several open problems and research directions regarding how MRTA evolves when there is a limited energy source or harvesting, how decentralized MRTA mechanisms are involved in robot intelligent decision-making, and how hybrid, learning-based game-theoretical models ease MRTA problems in real-time and on a large scale. Furthermore, this survey also aims to examine the possibility of integrating game theory-based MRTA methodologies including Nash, Stackelberg, and coalition games with hybrid multi-objective optimization methods. Based on the steps taken, this study collects various pieces of information from the literature to form a cohesive MRTA survey. Hence, the shortcomings of existing methods can be identified so that potential research directions can be outlined, particularly research related to energy-efficient, decentralized autonomous MRTA using game theory concepts.