AI-Enabled Disaster Response Planning for Multi-robot and Autonomous Systems via Task Scheduling and Path-Finding

With the increasing interest in autonomous vehicles and robots, new systems that can handle heterogeneous Multi-Robot and Autonomous Systems (MRAS) are needed. In this paper, we want to propose a system to coordinate and manage a generic unmanned team of land and aerial heterogeneous robots in a highly dynamic environment, to address emergencies and hazardous environments, such as in Disaster Response (DR) scenarios via a rapid scheduling and allocation algorithm. To do this we propose a greedy heuristic algorithm to solve this dynamic problem while also considering all the major constraints a fleet of robots could incur, by decomposing the whole problem and optimising over each of its sub-parts.