Harnessing coherence of area decomposition and semantic shared spaces for task allocation in a robotic fleet

Task allocation is a fundamental problem in multi-robot systems where heterogeneous robots cooperate to perform a complex mission.A general requirement in a task allocation algorithm is to find an optimal set of robots to execute a certain task.This paper presents the work that harnesses an area decomposition algorithm,and a space-based middleware to facilitate task allocation process in unstructured and dynamic environments.To reduce spatial interference between robots,area decomposition algorithm divides a working area into cells which are then dynamically assigned to robots.In addition,coordination and collaboration among distributed robots are realized through a space-based middleware.For this purpose,the space-based middleware is extended with a semantic model of robot capabilities to improve task selection in terms of flexibility,scalability,and reduced communication overhead during task allocation.In this way a framework which exploits the synergy of area decomposition and semantically enriched space-based approach is created.We conducted performance tests in a specific precision agriculture use case focusing on the utilization of a robotic fleet for weed control introduced in the European Project RHEA–Robot Fleets for Highly Effective Agriculture and Forestry Management.