Collaborative Coverage Strategy Using Multiple UAVs-UGVs in CRN Mapping

Chemical, Radiological, and Nuclear (CRN) contamination poses a significant threat, potentially leading to mass casualties and long-term environmental repercussions. This paper presents a collaborative framework utilizing a heterogeneous coverage control approach to measure and generate an estimated density distribution map of a designated area. Multiple Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are deployed strategically within partitioned regions, determined through weighted Voronoi tessellation. This method integrates both the robots’ internal parameters and environmental factors. The distinct operational domains of UAVs and UGVs facilitate region decomposition by accounting for variations in CRN dispersion, obstacle representation, and environmental conditions. The resulting cross-partitioned regions are systematically merged to enhance robot distribution efficiency. Each robot autonomously measures within its allocated region, updates contamination data, and generates a dispersion map. The proposed strategy enables an adaptive robot distribution, eliminating uncontaminated grids and improving mapping accuracy. Compared to existing methods, including homogeneous schemes, our approach reduces data variance in CRN-contaminated regions while maintaining mapping efficiency.