Navigating the Future: An In-Depth Exploration of Quantum Computing in Swarm Intelligence based Multi Robot Systems

Abstract

In recent times, Multi-Robot Systems (MRS) have garnered extensive attention for their versatility and potential to tackle diverse real-world challenges. Among the myriad problems these systems aim to resolve the Multi-Robot Task Allocation (MRTA) stands out due to its pivotal role in optimizing collective robot performance. MRTA focuses on the efficient distribution of tasks among a group of robots, with objectives often centered around minimizing operational time or maximizing efficiency. Delving into optimization-based approaches, we critically review various studies to highlight their strengths and limitations. This examination reveals the innovative strategies that have emerged in the field, underscoring both the achievements and the persisting challenges within MRTA research. By identifying these gaps, we aim to outline potential directions for future inquiry, suggesting pathways for advancements in MRS efficiency and application breadth using quantum computing. The integration of quantum computing into swarm-based multi-robot systems is an emerging interdisciplinary field that promises to enhance the capabilities of robotic collectives. By leveraging principles of quantum mechanics, such as superposition and entanglement, these systems can achieve more efficient coordination, decision-making, and problem-solving.
Furthermore, this paper presents evolution of MRTA strategies over recent years, identifying prevalent methods and noting shifts in research focus. Through this analysis, we aim to expose a extensive overview of the state-of-the-art in MRTA, encouraging further exploration and interdisciplinary collaboration. The integration of quantum computing into Multi-Robot Task Allocation (MRTA) represents a significant advancement in the field, promising to enhance the efficiency and capabilities.