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A collaborative scheme of multiple ground and aerial robots applying a heterogeneous coverage control approach is presented. It aims to provide a density map of a contaminated area from hazardous material. Compared to a homogeneous scheme, heterogeneity enhances the coverage level by minimizing error and variance due to the estimation process. In t...
Citations
... The homogeneous multi-robot system mostly addresses the time-related problems while the heterogeneous is possibly enhanced to solve the space-constraints and other complex issues. Furthermore, the heterogeneous multi-robot systems, comprising UAVs and UGVs, offer versatility and resilience in challenging CRN environments [33]. Collaborative Unmanned Aerial-Ground Vehicle Systems (UAGVS) exemplify the potential of synergistic interactions between aerial and ground robots for comprehensive CRN mapping [34]- [36]. ...
... In development, [60] added a strategy to find the source's location over mapping by considering particle coefficient and wind factor. Meanwhile, multi-robot deployment in radiative field mapping mainly adapts the Voronoi-based coverage with a Gaussian mixture as an estimation model [33]. ...
... As mentioned above, coverage control strategies leave several questions and problems unsolved. Practically, no robots are precisely similar in terms of sensor readings and footprints [61], [62], actuation errors [63], [64], or their specific configurations [33], [65], [66]. In [67], the authors first assign heterogeneity issues as a problem in optimizing coverage strategy. ...
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.
