Partitioning Known Environments for Multi-Robot Task Allocation Using Genetic Algorithms

Abstract

A common challenge facing emergency services, particularly in response to fires and/or earthquakes, is the location and subsequent extraction of people from hazardous buildings in a timely manner. This usually requires emergency service workers to enter the building and put their own lives at risk, even when there may be no people to extract. However, given recent advances in autonomous robotics, drones are expected to help humans in tasks such as search and rescue, and similar tasks, where coverage and time are key parameters. The aim is to complete a comprehensive search of the environment as quickly as feasible. Using multiple drones rather than a single drone can reduce search time, although performance can be poor if the searching is non-coordinated. Therefore, partitioning a terrain is important in order to effectively distribute the drone search work so that good coverage can be achieved in a reasonable amount of time and redundant searching is eliminated. In this paper a novel Square Based Terrain Partitioning (SBTP) algorithm is presented using a genetic algorithm to partition a known environment into multiple domains in a multi-robot exploration system. In addition, a second genetic algorithm is presented to allocate the domain search workload such that, given a certain number of drones, the overall search time is minimized.