In the near future, smart cities are expected to become more prevalent, with Uncrewed Aerial Vehicles (UAVs) playing a key role in making cities more efficient and sustainable. Effective path planning is essential for the safe and efficient integration of drones into urban airspace. However, one potential limitation of UAVs is that they may not have sufficient computing power to perform real-time contingency planning when encountering obstacles. To address this challenge, this work proposes edge-assisted offloading scenarios where contingency planning is considered as a resource-intensive task that can be offloaded to nearby edge nodes. We implemented and compared various strategies for generating offloading plans in a robot swarm simulator based on latency and cost metrics. Our evaluation revealed that the offloading plans generated using the genetic algorithm tended to perform better in terms of average latency or cost per offloading, albeit with higher runtime overhead compared to the other strategies.