The introduction of Robotic & Autonomous Systems (RAS) in modern combat seems inevitable, with clear advantages like reduced risk and extensification of personnel. To scope this research, persistent reconnaissance with heterogeneous Unmanned Aerial Vehicles (UAVs) is selected, being one of the more prominent applications. Despite continuous efforts developing advanced hardware and algorithms, real-world implementations are still lacking. The root cause seems to be that state-of-the-art algorithms deal insufficiently with the high dynamics and uncertainty in a military environment. Currently, the military uses intent-based Command & Control (C2) to deal with precisely these challenges, as they are inherently tied to combat. Therefore, a conversion of the communicative principles of C2 towards a mathematical approach applicable to RAS seems promising, of which intent-based coordination is the result. To be able to deal with the high dynamics and uncertainty, three requirements are formulated. First, flexibility is needed to revise the solution locally. Secondly, robustness against unreliable communications is necessary, and thirdly, scalability is required to ensure the performance can also be maintained for larger Areas of Interest (AOIs) and larger teams of UAVs. The Single-Agent Reconnaissance Problem (SARP) and Multi-Agent Reconnaissance Problem (MARP) are formulated as a compact combination between the visitation frequency and coverage level approach for persistent reconnaissance. Based on advancements made on teamwork and organizations for Multi-Robot Systems (MRSs), a coordinative method is formulated. This coordinative method partitions an AOI for the MARP into smaller disjoint subsets, such that separate SARPs can be solved independently by each UAV. The key contribution of this research is that this coordinative method functions based on intent, enabling the required flexibility, robustness, and scalability. It does so by constructing a hierarchy of supervisors that perform distributed cooperation on overlapping subsets. This distributed problem is solved using the novel Complex Concurrent Bounding (CCB), which is an adjusted version of Concurrent Forward-Bounding (ConcFB) for Distributed Constraint Optimization Problems (DCOPs) with complex local problems. Additionally, a lower bound is generated to benchmark the obtained solutions, based on the pricing step of branch & price, by applying column generation to a reformulated version of the MARP. Intent-based coordination shows flexibility against perturbations of the AOI. Especially when changes are spread out, it is not necessary to revise the solution as a whole immediately. Furthermore, if the cooperation is preemptively terminated due to failing communication, robustness is observed against the resulting suboptimal subsets. Especially for higher levels in the hierarchy, the suboptimal solutions can partially be corrected by lower levels. Lastly, the method shows a sublinear growth in computation time for increasingly larger problem instances. As such, intent-based coordination provides an exciting approach to maintain the performance of RAS even in more challenging environments.