Heuristic algorithms for planning and scheduling disaster response operation with an heterogeneous multi-robot autonomous system

The demand for advanced systems capable of managing heterogeneous Multi-Robot Systems (MRS) is rapidly increasing in parallel with the growth of autonomous vehicles and robotics technologies. This research addresses this need by presenting a comprehensive system designed to coordinate and manage teams of heterogeneous unmanned land and aerial robots operating in highly dynamic and unpredictable environments, such as those encountered in Disaster Response (DR) scenarios. A central focus of our work lies in the development of a robust and adaptable task generation, allocation, and scheduling framework. We propose a greedy heuristic algorithm to tackle the dynamic task allocation problem, ensuring that the system can effectively assign tasks while considering the key constraints faced by robotic fleets. These constraints include limited resources, task priorities, and varying capabilities among heterogeneous agents. Our approach is grounded in the decomposition of the overarching problem into smaller, more manageable sub-problems, thus optimizing each component individually, ensuring an efficient, scalable, and flexible solution. The proposed framework is particularly suited for complex scenarios where timely and accurate decisions are crucial. By optimizing the allocation of resources and tasks, our system enables heterogeneous teams of robots to operate collaboratively and autonomously, minimizing delays and maximizing mission success rates. Through this research, we aim to contribute to the development of resilient and intelligent MRS solutions, capable of addressing real-world challenges in disaster response and beyond.