MCO plan: Efficient coverage mission for multiple micro aerial vehicles modeled as agents

This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems. ; Micro aerial vehicle (MAV) fleets have gained essential recognition in the decision schemes for precision agriculture, disaster management, and other coverage missions. However, they have some challenges in becoming massively deployed. One of them is resource management in restricted workspaces. This paper proposes a plan to balance resources when considering the practical use of MAVs and workspace in daily chores. The coverage mission plan is based on five stages: world abstraction, area partitioning, role allocation, task generation, and task allocation. The tasks are allocated according to agent roles, Master, Coordinator, or Operator (MCO), which describe their flight autonomy, connectivity, and decision skill. These roles are engaged with the partitioning based on the Voronoi-tessellation but extended to heterogeneous polygons. The advantages of the MCO Plan were evident compared with conventional Boustrophedon decomposition and clustering by K-means. The MCO plan achieved a balanced magnitude and trend of heterogeneity between both methods, involving MAVs with few or intermediate resources. The resulting efficiency was tested in the GAMA platform, with gained energy between 2% and 10% in the mission end. In addition, the MCO plan improved mission times while the connectivity was effectively held, even more, if the Firefly algorithm generated coverage paths. ; The authors are grateful to the Telematics Engineering Group (GIT) of the University of Cauca, the Ministry of Science Technology and Innovation of Colombia (Minciencias) for the Ph.D. support granted to Liseth Viviana Campo, as well as Project "Incremento de la oferta de prototipos tecnológicos en estado pre-comercial derivados de resultados de I + D para el fortalecimiento del sector agropecuario en el departamento del Cauca" funding by SGR (BPIN 2020000100098).