In a framework of multi-agent systems, the optimization of the coverage of a spatial area is of paramount importance due to its variety of uses in robotic applications. Most notability in roles where an agent (or a mobile robot) is preferable to a human or when a task is simply impossible for a human to perform . Typical applications for area coverage optimization in the field of mobile robotics include search and rescue , surveillance , environmental monitoring , cooperative estimation , and indoor navigation , among others. These problems are usually solved using an array of networked mobile agents operating collectively. Recently the implementation of area coverage algorithms has been restricted to a fleet of homogeneous robots at high monetary cost. The purpose of this research is to lower the costs of entry due to the selected robot platform by providing an easily accessible framework for heterogeneous robots that can be implemented both expediently and efficiently. Note that the authors will be using the terms robots and agents interchangeably from now on.