Authors - Rehab Hassan Bader, Issa A. Abed, Bayadir A.Isaa Abstract - Multi-Robot Systems (MRS) have developed as a disruptive technology in robotics, allowing multiple autonomous robots to work together to execute complex tasks. Multi-Robot Task Allocation (MRTA) is an important component of MRS since it involves distributing tasks to robots in a way that maximizes performance, efficiency, and resource use. Effective scheduling is crucial in MRTA because it determines the order and timing of task execution, which has a significant impact on overall system performance. This paper investigates current MRTA and scheduling strategies and algorithms, with an emphasis on the challenges raised by dynamic environments and the requirement for flexibility in task allocation. They discuss numerous approaches, including centralized and decentralized methods, constraint-based models, and reinforcement learning techniques, emphasizing the benefits and drawbacks. The combination of reallocation and rescheduling approaches is also being researched as a means of boosting system responsiveness to unanticipated changes. The findings suggest that future research should focus on developing hybrid models that take advantage of existing approaches to create more resilient and efficient multi-robot systems capable of operating in real-world scenarios.
Open Zoom
